Application program sorting method and apparatus

ABSTRACT

An application sorting method and apparatus are provided. The method includes: obtaining, a positive operation probability and positive operation feedback information of each of at least two data samples; calculating an uncertainty parameter of a positive operation probability of a first data sample based on the positive operation probabilities and the positive operation feedback information of the at least two data samples and feature indication information of at least one same feature in a plurality of features in the at least two data samples; and correcting the positive operation probability of the first data sample by using the uncertainty parameter of the positive operation probability; and sorting, based on corrected positive operation probabilities, application programs corresponding to the at least two data samples.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2017/113213, filed on Nov. 27, 2017, which claims priority toChinese Patent Application No. 201611259915.X, filed on Dec. 30, 2016.The disclosures of the aforementioned applications are herebyincorporated by reference in their entireties.

TECHNICAL FIELD

This application relates to the field of electronic technologies, and inparticular, to an application program sorting method and apparatus.

BACKGROUND

As terminal devices (such as smartphones and tablet computers) arepopularized, increasingly more application programs can be installed ona terminal device to provide different services for a user. Anapplication recommendation server may push an application program to aterminal device, and the terminal device displays, by using anapplication recommendation interface, the application programrecommended by the application recommendation server.

A sequence of an application program in the application recommendationinterface is crucial for whether a user performs a positive operation onthe application program. For example, the user is more likely todownload an application program displayed at a higher display positionin the application recommendation interface. The positive operationperformed by the user on the application program may be an operationsuch as tapping, downloading, or payment that can reflect a will of theuser to use the application program.

In the prior art, the following operations may be performed on eachapplication program: calculating a ratio of a quantity of forwardoperations performed on the application program to a total quantity offorward operations (namely, a quantity of forward operations performedon all the application programs), to obtain a positive operationprobability of the application program; and then sorting all theapplication programs based on the positive operation probabilities ofall the application programs.

However, “whether a user performs a positive operation on an applicationprogram” is affected by many factors. In the prior art, the positiveoperation probability obtained through calculation based on the quantityof forward operations performed on the application program and the totalquantity of forward operations is inaccurate. After the applicationprograms are sorted based on the inaccurate positive operationprobabilities, a sorting sequence of the application program cannotaccurately reflect a possibility that the user performs a positiveoperation on the application program.

SUMMARY

An application program sorting method and apparatus are disclosed toimprove accuracy of a positive operation probability, which can moreaccurately reflect a possibility that a user performs a positiveoperation on an application program.

To achieve the foregoing objective, the following technical solutionsare disclosed.

According to a first aspect, an application program sorting method, andthe application program sorting method includes: obtaining a positiveoperation probability and positive operation feedback information ofeach of at least two data samples, where the data sample corresponds toone user and one application program, the data sample includes featureindication information of a plurality of features, the plurality offeatures in the data sample are respectively the same as a plurality offeatures in another data sample, the another data sample is any one ofthe at least two data samples except the data sample, the positiveoperation probability of the data sample is a probability that the userperforms a positive operation on the application program in a presettime period, and the positive operation feedback information of the datasample is used to indicate whether the user has performed a positiveoperation on the application program in the preset time period;performing the following operations on each of the at least two datasamples: calculating an uncertainty parameter of a positive operationprobability of a first data sample based on the positive operationprobabilities of the at least two data samples, the positive operationfeedback information of the at least two data samples, and featureindication information of at least one same feature in a plurality offeatures in the at least two data samples; and correcting the positiveoperation probability of the first data sample by using the uncertaintyparameter of the positive operation probability of the first datasample, to obtain a corrected positive operation probability of thefirst data sample, where the first data sample is any one of the atleast two data samples; and sorting, based on corrected positiveoperation probabilities of the at least two data samples, allapplication programs corresponding to the at least two data samples.

The data sample corresponds to one user and one application program, andthe data sample includes the feature indication information of theplurality of features. The plurality of features in the data sample mayinclude indication information of each of at least three features, theat least three features include at least one feature of the user, atleast one feature of the application program, and a feature of at leastone scenario, and the at least one scenario is a scenario in which theuser operates the application program. The plurality of features in thedata sample are respectively the same as the plurality of features inthe another data sample, and the another data sample is any one of theat least two data samples except the data sample. For example, each ofthe at least two data samples may include the at least three features.The positive operation probability of the data sample is the probabilitythat the user corresponding to the data sample performs a positiveoperation on the application program in the preset time period. Thepositive operation feedback information of the data sample is used toindicate whether the user corresponding to the data sample has performeda positive operation on the application program in the preset timeperiod. Duration of the preset time period may be preset. The feature ofthe at least one scenario may be environmental parameters, such as time,a position, and weather in which the user operates the applicationprogram, that may affect “whether the user performs a positive operationon the application program”.

In this solution, the uncertainty parameter of the positive operationprobability of each data sample may be calculated based on the positiveoperation probabilities of the at least two data samples, the positiveoperation feedback information of the at least two data samples, and thefeature indication information of the at least one same feature in theplurality of features in the at least two data samples, and the positiveoperation probability of the data sample is then corrected by using theuncertainty parameter of each positive operation probability, to obtainthe corrected positive operation probability of the data sample.Compared with the uncorrected positive operation probability, thecorrected positive operation probability can more accurately reflect thepossibility that the user performs a positive operation on theapplication program. Therefore, compared with an application programsorting result obtained after the application programs are sorted byusing the uncorrected positive operation probabilities, an applicationprogram sorting result obtained after all the application programscorresponding to the at least two data samples are sorted based on thecorrected positive operation probabilities of the at least two datasamples can increase the possibility that the user performs a positiveoperation on the application program.

In an implementation of the first aspect, considering that in additionto the positive operation probability, factors affecting “whether theuser performs a positive operation on the application program” mayinclude a display position at which the application program is locatedin an application recommendation interface of a terminal device, a userattention degree of the display position is used to represent thepossibility that the user performs a positive operation on theapplication program displayed at the display position. When theapplication program is displayed at different display positions,possibilities of performing a positive operation on the applicationprogram are different. For example, when a same application program isseparately displayed at a higher display position and a lower displayposition, the user is usually more likely to perform a positiveoperation on the application program at the higher display position. Inother words, both uncertainties of positive operation probabilities anddisplay positions at which application programs are located in theapplication recommendation interface of the terminal device affectaccuracy of sorting the application programs by an applicationrecommendation server. To further improve accuracy of sorting theapplication programs by the application recommendation server, whencorrecting the positive operation probability based on the uncertaintyparameter of the positive operation probability is considered, thepositive operation probability may be further corrected based on a userattention degree of each display position. Specifically, the “correctingthe positive operation probability of the first data sample by using theuncertainty parameter of the positive operation probability of the firstdata sample, to obtain a corrected positive operation probability of thefirst data sample” may include: correcting the positive operationprobability of the first data sample by using the uncertainty parameterof the positive operation probability of the first data sample and theuser attention degree of each display position, to obtain the correctedpositive operation probability of the first data sample.

In an implementation of the first aspect, the “calculating anuncertainty parameter of a positive operation probability of a firstdata sample based on the positive operation probabilities of the atleast two data samples, the positive operation feedback information ofthe at least two data samples, and feature indication information of atleast one same feature in a plurality of features in the at least twodata samples” may include: performing the following operation on each ofthe at least one same feature: calculating a weight of a first samefeature based on the positive operation probabilities of the at leasttwo data samples, the positive operation feedback information of the atleast two data samples, and feature indication information of the firstsame feature in each of the at least two data samples, where the firstsame feature is any one of the at least one same feature; andcalculating the uncertainty parameter of the positive operationprobability of the first data sample based on a weight of the at leastone same feature and the feature indication information of the at leastone same feature included in the first data sample.

It should be noted that a weight of any same feature in the at least twodata samples may be obtained based on a quantity of data samples thatare in all the data samples and in which indication information of thesame feature is 1, the positive operation probability of each datasample, and the positive operation feedback information of each datasample. When the feature indication information of any same feature is0, it indicates that the same feature has no impact on “whether the userperforms a positive operation on the application program”.Alternatively, when the feature indication information of any samefeature is 1, it indicates that the same feature may “whether the userperforms a positive operation on the application program”. For example,a larger quantity of forward data samples (data samples whose positiveoperation feedback information is 1) that are in forward data samples inthe at least two data samples and in which the feature indicationinformation of any same feature is 1 indicates a higher weight of thesame feature, thereby indicating a greater impact of the same feature on“whether the user performs a positive operation on the applicationprogram”.

In an implementation of the first aspect, the at least one same featureincludes all features in the at least two data samples, and the firstsame feature is any one of the at least one same feature. The“calculating a weight of a first same feature based on the positiveoperation probabilities of the at least two data samples, the positiveoperation feedback information of the at least two data samples, andfeature indication information of the first same feature in each of theat least two data samples” may include:

calculating a weight n_(k) of a k^(th) Same feature in the at least twodata samples by using

${n_{k} = {\sum\limits_{j = 1}^{n}{\sum\limits_{i = 1}^{m}\left( {\left( {P_{({i,j})} - y_{({i,j})}} \right) \times a_{{({i,j})} - k}} \right)^{2}}}},$where k∈{1, 2, . . . , q}, is used to represent a quantity of featuresin X_((i,j)), and q≥3, where X_((i,j)) is used to represent a datasample corresponding to a user i and an application program j, i∈{1, 2,. . . , m}, m is used to represent a quantity of users, m≥2, j∈{1, 2, .. . , n}, n is used to represent a quantity of application programs, andn≥2; P_((i,j)) is used to represent a positive operation probability ofX_((i,j)) and y_((i,j)) is used to represent positive operation feedbackinformation of X_((i,j)); and a_((i,j)-k) is used to represent featureindication information of the k^(th) same feature x_((i,j)-k) inX_((i,j)).

A smaller difference between the positive operation probability and thepositive operation feedback information of any one of the at least twodata samples indicates a more accurate positive operation probability ofthe data sample. When the feature indication information a_((i,j)-k) ofthe k^(th) same feature x_((i,j)-k) in the data sample X_((i,j)) is 0,it indicates that the k^(th) same feature has no impact on the positiveoperation probability P_((i,j)) of the data sample X_((i,j)).(P_((i,j)))−y_((i,j)))×a_((i,j)-k) is 0, that is, the k^(th) samefeature x_((i,j)-k) has no impact on the weight of x_((i,j)-k). When thefeature indication information a_((i,j)-k) of the k^(th) same featurex_((i,j)-k) in the data sample X_((i,j)) is 1, the weight of x_((i,j)-k)may be calculated based on a difference (that is, a differenceP_((i,j))−y_((i,j)) between P_((i,j)) and y_((i,j))) between thepositive operation probability P_((i,j)) and the positive operationfeedback information y_((i,j)) of the data sample X_((i,j)). Forexample, the positive operation probability P_((i,j)) is obtainedthrough calculation, and the positive operation feedback informationy_((i,j)) is a real value. Therefore, a smaller differenceP_((i,j))−y_((i,j))) between the positive operation probabilityP_((i,j)) and the positive operation feedback information y_((i,j)) ofthe data sample X_((i,j)) indicates a more accurate positive operationprobability P_((i,j)) of the data sample. In this case, the positiveoperation probability has a smaller impact on the weight n_(k) of thek^(th) same feature x_((i,j)-k) in the data sample X_((i,j)) andtherefore the weight n_(k) of the k^(th) same feature x_((i,j)-k) islower in all the data samples.

In an implementation of the first aspect, the at least one same featureincludes all the features in the at least two data samples; and thecalculating the uncertainty parameter of the positive operationprobability of the first data sample based on a weight of the at leastone same feature and the feature indication information of the at leastone same feature included in the first data sample may include:

calculating an uncertainty parameter uc_((i,j)) of the positiveoperation probability of the data sample X_((i,j)) by using

${u_{({i,j})} = {\sum\limits_{k = 1}^{q}\frac{a_{{({i,j})} - k}}{\sqrt{n_{k}}}}},$where the first data sample is the data sample X_((i,j)) of the user iand the application program j, i∈{1, 2, . . . , m}, m is used torepresent the quantity of users, m≥2, j∈{1, 2, . . . , n}, n is used torepresent the quantity of application programs, and n≥2; k∈{1, 2, . . ., q}, q is used to represent the quantity of features in X_((i,j)) andq≤3; n_(k) is used to represent the weight of the k^(th) same feature inX_((i,j)); and a_((i,j)-k) is used to represent the feature indicationinformation of the k^(th) same feature x_((i,j)-k) in X_((i,j)).

When the feature indication information a_((i,j)-k) of the k^(th) samefeature x_((i,j)-k) in the data sample X_((i,j)) is 0, the k^(th) samefeature x_((i,j)-k) has no impact the uncertainty parameter uc_((i,j))of the positive operation probability of the data sample X_((i,j)). Whenthe feature indication information a_((i,j)-k) of the k^(th) samefeature x_((i,j)-k) in the data sample X_((i,j)) is 1, the k^(th) samefeature x_((i,j)-k) may affect the uncertainty parameter uc_((i,j)) ofthe positive operation probability of the data sample X_((i,j)). Forexample, when the feature indication information a_((i,j)-k) of thek^(th) same feature x_((i,j)-k) in the data sample X_((i,j)) is 1, ahigher weight n_(k) of the k^(th) same feature x_((i,j)-k) indicates asmaller uncertainty parameter uc_((i,j)) of the positive operationprobability of the data sample X_((i,j)).

In an implementation of the first aspect, the uncertainty parameter ofthe positive operation probability of the first data sample may befurther normalized in this application. For example, a method for“normalizing the uncertainty parameter of the positive operationprobability of the first data sample” in this application is describedherein by using an example in which the uncertainty parameter of thepositive operation probability of the first data sample X_((i,j)) isnormalized to a value ranging from 0 to 1. The method for normalizingthe uncertainty parameter of the positive operation probability of thefirst data sample includes: if the uncertainty parameter uc_((i,j)) ofthe positive operation probability of the data sample X_((i,j)) isgreater than or equal to 1, determining the uncertainty parameter of thepositive operation probability of the data sample X_((i,j)) as 1; or ifthe uncertainty parameter uc_((i,j)) of the positive operationprobability of the data sample X_((i,j)) is less than 1, determiningthat a normalized uncertainty parameter of the positive operationprobability of the data sample X_((i,j)) is still uc_((i,j)).Alternatively, the uncertainty parameter of the positive operationprobability of the data sample X_((i,j)) may be normalized by using

${{uc}_{({i,j})}^{1} = \frac{{uc}_{({i,j})}}{\max\left\{ {uc} \right\}}},$to obtain a normalized uncertainty parameter uc_((i,j)) ¹ of thepositive operation probability of the data sample X_((i,j)), wheremax{uc} is used to represent a maximum uncertainty parameter inuncertainty parameters of the positive operation probabilities of allthe data samples. The first data sample is the data sample X_((i,j)) ofthe user i and the application program j, i∈{1, 2, . . . , m} m is usedto represent the quantity of users, m≥2, j∈{1, 2, . . . , n} n is usedto represent the quantity of application programs, and n≥2.

In an implementation of the first aspect, the correcting the positiveoperation probability of the first data sample by using the uncertaintyparameter of the positive operation probability of the first datasample, to obtain a corrected positive operation probability of thefirst data sample may include:

correcting the positive operation probability P_((i,j)) of the datasample X_((i,j)) by using P_((i,j)) ¹=P_((i,j))×uc_((i,j)), to obtain acorrected positive operation probability P_((i,j)) ¹ of X_((i,j)), wherethe first data sample is the data sample X_((i,j)) of the user i and theapplication program j, i∈{1, 2, . . . , m}, m is used to represent thequantity of users, m≥2, j∈{1, 2, . . . , n} n is used to represent thequantity of application programs, and n≥2; and uc_((i,j)) is used torepresent the uncertainty parameter of the positive operationprobability P_((i,j)) of X_((i,j)).

The positive operation probability P_((i,j)) that is of the data sampleX_((i,j)) and that is obtained based on the at least two data samplesand the positive operation feedback information corresponding to the atleast two data samples is inaccurate. However, an uncertainty of thepositive operation probability P_((i,j)) of the data sample X_((i,j))may be corrected based on the uncertainty parameter uc_((i,j)) of thepositive operation probability P_((i,j)) by using P_((i,j))×uc_((i,j))to obtain the corrected positive operation probability P_((i,j)) ¹ ofthe data sample X_((i,j)). For example, the uncertainty parameteruc_((i,j)) of the positive operation probability P_((i,j)) may be anormalized uncertainty parameter uc_((i,j)) ¹. For example, when theuncertainty parameter uc_((i,j)) of positive operation the positiveoperation probability is normalized to a value ranging from 0 to 1, alarger uncertainty parameter uc_((i,j)) ¹ indicates a larger correctedpositive operation probability P_((i,j)) ¹. To be specific, apossibility that the user i in the data sample X_((i,j)) performs apositive operation on the application program j is larger. Therefore, ifthe application program j is sorted based on the corrected positiveoperation probability P_((i,j)) ¹, an obtained sorting position of theapplication program j ranks higher, and a display position of theapplication program j in the application recommendation interface alsoranks higher.

In an implementation of the first aspect, the correcting the positiveoperation probability of the first data sample by using the uncertaintyparameter of the positive operation probability of the first data sampleand the user attention degree of each display position, to obtain thecorrected positive operation probability of the first data sample mayinclude:

correcting a positive operation probability P_((i,j)) of a data sampleX_((i,j)) by using P_((i,j)) ^((l+1))=P_((i,j))×uc_((i,j)))^(B) and

${B = {\sum\limits_{l = 1}^{T}{b\left\{ {{pos}\left( {j,l} \right)} \right\}}}},$to obtain a corrected positive operation probability P_((i,j)) ^((l+1))of X_((i,j)), where the first data sample is the data sample X_((i,j))of a user i and an application program j, i∈{1, 2, . . . , m}, m is usedto represent a quantity of users, m≥2, j∈{1, 2, . . . , n}, n is used torepresent a quantity of application programs, and n≥2; uc_((i,j)) isused to represent an uncertainty parameter of the positive operationprobability P_((i,j)) of X_((i,j)) pos(j,l) is used to represent adisplay position at which the application program j is displayed for thel^(th) time in the application recommendation interface; and b{pos(j,l)}is used to represent a user attention degree of the display positionpos(j,l), l∈{1, 2, . . . , T}, T is used to represent a quantity oftimes of displaying the application program j in the applicationrecommendation interface, and T≥1.

The display position also affects the positive operation probability“whether the user performs a positive operation on the applicationprogram”. A higher user attention degree of the display positionindicates a higher probability that the user performs a positiveoperation on the application program recommended at the displayposition. The positive operation probability of the data sample iscorrected by using the uncertainty parameter of the positive operationprobability of each of the at least two data samples and with referenceto a user attention degree of a display position at which theapplication program is displayed each time in the applicationrecommendation interface, so that the positive operation probability canmore accurately reflect the possibility that the user performs apositive operation on the application program. For example, when theuncertainty parameter uc_((i,j)) of the positive operation and the userattention degree b{pos(j,l)} of the display position pos(j,l) each arenormalized to a value ranging from 0 to 1, and the positive operationprobability P_((i,j)) is a value ranging from 0 to 1, a higher userattention degree b{pos(j,l)} of the display position pos(j,l) indicatesa larger B, and therefore indicates a smaller P_((i,j))×(uc_((i,j)))^(B)and a smaller corrected positive operation probability P_((i,j))^((l+1)) of the data sample. To be specific, the possibility that theuser i in the data sample X_((i,j)) performs a positive operation on theapplication program j is smaller. Therefore, if the application programj is sorted based on the corrected positive operation probabilityP_((i,j)) ^((l+1)), an obtained sorting position of the applicationprogram j ranks lower, and a display position of the application programj in the application recommendation interface also ranks lower.

It should be noted that the corrected positive operation probabilityP_((i,j)) ^((l+1)) of the data sample is a possibility that the user iperforms a positive operation on the application program j when theapplication program j is displayed at the display position for the(l+1)^(th) time pos(j,l+1) in the application recommendation interface.

In an implementation of the first aspect, the sorting, based oncorrected positive operation probabilities of the at least two datasamples, all application programs corresponding to the at least two datasamples may include: determining at least two second data samples fromthe at least two data samples, where each of the at least two seconddata samples corresponds to a first user and one application program;and sorting, based on corrected positive operation probabilities of theat least two second data samples, all application programs correspondingto the at least two second data samples, where a sorting result obtainedafter all the application programs corresponding to the at least twosecond data samples are sorted is used to recommend, to the first user,all the application programs corresponding to the at least two seconddata samples.

Because the at least two second data samples are data samplescorresponding to the first user, all the application programscorresponding to the at least two second data samples are sorted for thefirst user by using a corrected positive operation probability of eachof the at least two second data samples, so that an application programsorting result meeting a user preference of the first user may beobtained. That is, a possibility that the first user performs a positiveoperation on the application program displayed in the applicationrecommendation interface of the terminal device of the first user islarger.

In an implementation of the first aspect, “calculating the userattention degree of each display position that is used to display theapplication program and that is in the application recommendationinterface of the terminal device” may be implemented in the followingtwo manners.

Manner 1: A quantity of forward operations performed in a time period onan application program displayed at each display position is obtained.The quantity of forward operations performed on the application programat each display position is used to represent the user attention degreeof the display position.

Manner 2: A quantity of forward operations performed in a time period onan application program displayed at each display position is obtained,and a ratio of the quantity of forward operations performed on theapplication program displayed at each display position to a quantity offorward operations performed on an application program displayed at afirst display position is calculated. A ratio of a quantity of forwardoperations performed on an application program displayed at any displayposition to the quantity of forward operations performed on theapplication program displayed at the first display position is used torepresent the user attention degree of the display position. The firstdisplay position is a display position in the display positions, where amaximum quantity of forward operations are performed on an applicationprogram displayed at the display position.

The “obtained quantity of forward operations performed on theapplication program displayed at each display position” may be aquantity that is of forward operations performed on the applicationprogram displayed at each display position and that is obtained throughstatistics collection in a time period after the application program isdisplayed at each display position according to a random sorting resultafter the application programs are randomly sorted; or may be a quantitythat is of forward operations performed on the application programdisplayed at each display position and that is obtained throughstatistics collection in a time period after the application program isdisplayed at each display position according to a sorting result afterthe application programs are sorted by using the application programsorting method provided in this application; or may be a quantity thatis of forward operations performed on the application program displayedat each display position and that is obtained through statisticscollection in a time period after the application program is displayedat each display position according to a sorting result after theapplication programs are sorted by using any other sorting method. Thetime period may be before the application recommendation servercalculates the user attention degree of each display position, andduration of the time period may be preset.

It should be noted that the “obtained quantity of forward operationsperformed on the application program displayed at each display position”may be a quantity that is obtained through statistics collection andthat is of forward operations performed by all users on the applicationprogram displayed at each display position; or may be a quantity that isobtained through statistics collection and that is of forward operationsperformed by a user randomly sampled from some users on the applicationprogram displayed at each display position, instead of a quantity offorward operations performed by a specific user on the applicationprogram displayed at each display position.

The “application program displayed at each display position” may be asame or different random application program. The “obtained quantity offorward operations performed on the application program displayed ateach display position” may be a quantity that is obtained throughstatistics collection and that is of forward operations performed on asame application program displayed at each display position; or may be aquantity of forward operations performed on different applicationprograms displayed at each display position, instead of a quantity thatis obtained through statistics collection and that is of forwardoperations performed on a specific application program displayed at eachdisplay position.

In an implementation of the first aspect, “the obtaining, by anapplication recommendation server, a positive operation probability andpositive operation feedback information of each of at least two datasamples” may include: obtaining, by the application recommendationserver, historical operation information of at least two applicationprograms of each of at least two terminal devices, to obtain the atleast two data samples and the positive operation feedback informationof each of the at least two data samples; collecting statistics aboutthe at least two data samples and the positive operation feedbackinformation of each of the at least two data samples, to obtain positiveoperation proportion values of all features in the at least two datasamples; and performing the following operation on each of the at leasttwo data samples: calculating the positive operation probability of eachdata sample based on the positive operation proportion values of all thefeatures in the at least two data samples.

The k^(th) same feature x_((i,j)-k) is used as an example. A positiveoperation proportion value W_(k) of the k^(th) same feature x_((i,j)-k)may represent a degree of an impact of the k^(th) same featureX_((i,j)-k) on “whether user i performs a positive operation on theapplication program j”. A larger positive operation proportion valuew_(k) of x_((i,j)-k) indicates a greater impact of the k^(th) samefeature x_((i,j)-k) on “whether the user performs a positive operationon the application program”.

According to a second aspect, this application provides an applicationprogram sorting apparatus, including an obtaining module, a calculationmodule, a correction module, and a sorting module. The obtaining modulemay be configured to obtain a positive operation probability andpositive operation feedback information of each of at least two datasamples, where each data sample corresponds to one user and oneapplication program, the at least two data samples include indicationinformation of a plurality of same features, the data sample includesfeature indication information of a plurality of features, the pluralityof features in the data sample are respectively the same as a pluralityof features in another data sample, the another data sample is any oneof the at least two data samples except the data sample, the positiveoperation probability of the data sample is a probability that the usercorresponding to the data sample performs a positive operation on theapplication program in a preset time period, and the positive operationfeedback information of the data sample is used to indicate whether theuser has performed a positive operation on the application program inthe preset time period. The calculation module may be configured toperform the following operation on each of the at least two datasamples: calculating an uncertainty parameter of a positive operationprobability of a first data sample based on the positive operationprobabilities of the at least two data samples, the positive operationfeedback information of the at least two data samples, and featureindication information of at least one same feature in a plurality offeatures in the at least two data samples, where the positive operationprobabilities and the positive operation feedback information areobtained by the obtaining module, and the first data sample is any oneof the at least two data samples. The correction module may beconfigured to perform the following operation on each of the at leasttwo data samples: correcting the positive operation probability of thefirst data sample by using the uncertainty parameter that is of thepositive operation probability of the first data sample and that isobtained through calculation by the calculation module, to obtain acorrected positive operation probability of the first data sample. Thesorting module may be configured to sort, based on positive operationprobabilities that are of the at least two data samples and that areobtained through correction by the correction module, all applicationprograms corresponding to the at least two data samples.

In an implementation of the second aspect, the calculation module may befurther configured to: before the correction module corrects thepositive operation probability of the first data sample by using theuncertainty parameter that is of the positive operation probability ofthe first data sample and that is obtained through calculation by thecalculation module, to obtain the corrected positive operationprobability of the first data sample, calculate a user attention degreeof each display position that is used to display an application programand that is in an application recommendation interface of a terminaldevice, where the user attention degree of the display position is usedto represent a possibility that the user performs a positive operationon the application program displayed at the display position. Thecorrection module may be specifically configured to correct the positiveoperation probability of the first data sample by using the uncertaintyparameter that is of the positive operation probability of the firstdata sample and that is obtained through calculation by the calculationmodule and the user attention degree that is of each display positionand that is obtained through calculation by the calculation module, toobtain the corrected positive operation probability of the first datasample.

In an implementation of the second aspect, the calculation module mayinclude: a weight calculation submodule and an uncertainty parametercalculation submodule. The weight calculation submodule is configured toperform the following operation on each of the at least one samefeature: calculating a weight of a first same feature based on thepositive operation probabilities of the at least two data samples, thepositive operation feedback information of the at least two datasamples, and feature indication information of the first same feature ineach of the at least two data samples, where the first same feature isany one of the at least one same feature. The uncertainty parametercalculation submodule is configured to perform the following operationon each of the at least two data samples: calculating the uncertaintyparameter of the positive operation probability of the first data samplebased on the feature indication information of the at least one samefeature included in the first data sample and a weight that is of the atleast one same feature and that is obtained through calculation by theweight calculation submodule.

In an implementation of the second aspect, the at least one same featureincludes all features in the at least two data samples, and the weightcalculation submodule may be specifically configured to:

calculate a weight n_(k) of a k^(th) same feature in the at least twodata samples by using

${n_{k} = {\sum\limits_{j = 1}^{n}{\sum\limits_{i = 1}^{m}\left( {\left( {P_{({i,j})} - y_{({i,j})}} \right) \times \; a_{{({i,j})} - k}} \right)^{2}}}},$where k∈{1, 2, . . . , q}, q is used to represent a quantity of featuresin X_((i,j)), and q≥3, where X_((i,j)) is used to represent a datasample corresponding to a user i and an application program 1, i∈{1, 2,. . . , m}, m is used to represent a quantity of users, m≥2, j∈{1, 2, .. . , n}, n is used to represent a quantity of application programs, andn≥2; P_((i,j)) is used to represent a positive operation probability ofX_((i,j)), and y_((i,j)) is used to represent positive operationfeedback information of X_((i,j)); and a_((i,j)-k) is used to representfeature indication information of the k^(th) same feature x_((i,j)-k) inX_((i,j)).

In an implementation of the second aspect, the uncertainty parametercalculation submodule may be specifically configured to:

calculate an uncertainty parameter uc_((i,j)) of the positive operationprobability of the data sample X_((i,j)) by using

${{uc}_{({i,j})} = {\sum\limits_{k = 1}^{q}\frac{a_{{({i,j})} - k}}{\sqrt{n_{k}}}}},$where the first data sample is the data sample X_((i,j)) of the user iand the application program j, i∈{1, 2, . . . , m}, m is used torepresent the quantity of users, m≥2, j∈{1, 2, . . . , n}, n is used torepresent the quantity of application programs, and n≥2; k∈{1, 2, . . ., q}, q is used to represent the quantity of features in X_((i,j)) andq≥3: n_(k) is used to represent the weight of the k^(th) same feature inX_((i,j)); and a_((i,j)-k) is used to represent the feature indicationinformation of the k^(th) same feature x_((i,j)-k) in X_((i,j)).

In an implementation of the second aspect, the correction module may bespecifically configured to correct the positive operation probabilityP_((i,j)) of the data sample X_((i,j)) by using P_((i,j))¹=P_((i,j))×uc_((i,j)), to obtain a corrected positive operationprobability P_((i,j)) ¹ of X_((i,j)), where the first data sample is thedata sample X_((i,j)) of the user i and the application program j, i∈{1,2 . . . , m}, m is used to represent the quantity of users, m≥2, j∈{1,2, . . . , n} n is used to represent the quantity of applicationprograms, and n≥2; and uc_((i,j)) is used to represent the uncertaintyparameter of the positive operation probability P_((i,j)) of X_((i,j)).

In an implementation of the second aspect, the correction module may bespecifically configured to:

correct a positive operation probability P_((i,j)) of a data sampleX_((i,j)) by using P_((i,j)) ^((l+1))=P_((i,j))×(uc_((i,j)))^(B) and

${B = {\sum\limits_{l = 1}^{T}{b\left\{ {{pos}\left( {j,l} \right)} \right\}}}},$to obtain a corrected positive operation probability P_((i,j)) ^((l+1))of X_((i,j)), where the first data sample is the data sample X_((i,j))of a user i and an application program j, i∈{1, 2, . . . , m}, m is usedto represent a quantity of users, m≥2, j∈{1, 2, . . . , n}, n is used torepresent a quantity of application programs, and n≥2; uc_((i,j)) isused to represent an uncertainty parameter of the positive operationprobability P_((i,j)) of X_((i,j)); pos(j,l) is used to represent adisplay position at which the application program j is displayed for thel^(th) time in the application recommendation interface; and b{pos(j,l)}is used to represent a user attention degree of the display positionpos(j,l), l∈{1, 2, . . . , T}, T is used to represent a quantity oftimes of displaying the application program j in the applicationrecommendation interface, and T≥1.

In an implementation of the second aspect, the sorting module may bespecifically configured to: determine at least two second data samplesfrom the at least two data samples, where each of the at least twosecond data samples corresponds to a first user and one applicationprogram; and sort, based on corrected positive operation probabilitiesof the at least two second data samples, all application programscorresponding to the at least two second data samples. A sorting resultobtained after all the application programs corresponding to the atleast two second data samples are sorted is used to recommend, to thefirst user, all the application programs corresponding to the at leasttwo second data samples.

In this solution, data samples, namely, at least two second datasamples, corresponding to each user may be determined from the at leasttwo data samples, and all application programs corresponding to the atleast two second data samples are sorted based on corrected positiveoperation probabilities of the at least two second data samplescorresponding to the user. In this way, an application program may berecommended to each user in a targeted manner according to a sortingresult of the application programs corresponding to the user, so thatthe recommended application program better meets a preference of theuser.

In an implementation of the second aspect, the obtaining module may bespecifically configured to: obtain historical operation information ofat least two application programs of each of at least two terminaldevices, to obtain the at least two data samples and the positiveoperation feedback information of each of the at least two data samples;collect statistics about the at least two data samples and the positiveoperation feedback information of each of the at least two data samples,to obtain a positive operation proportion value of at least one samefeature in a plurality of features in the at least two data samples; andperform the following operation on each of the at least two datasamples: calculating the positive operation probability of the firstdata sample based on the positive operation proportion value of the atleast one same feature in the plurality of features in the at least twodata samples. The first data sample is any one of the at least two datasamples.

In an implementation of the second aspect, the calculation module isspecifically configured to obtain a quantity of times of forwardoperations performed in a time period on the application programdisplayed at each display position, where the quantity of forwardoperations performed on the application program at each display positionis used to represent the user attention degree of the display position.

In an implementation of the second aspect, the calculation module isspecifically configured to: obtain a quantity of forward operationsperformed in a time period on an application program displayed at eachdisplay position, and calculate a ratio of the quantity of forwardoperations performed on the application program displayed at eachdisplay position to a quantity of forward operations performed on anapplication program displayed at a first display position. A ratio of aquantity of forward operations performed on an application programdisplayed at any display position to the quantity of forward operationsperformed on the application program displayed at the first displayposition is used to represent the user attention degree of the displayposition. The first display position is a display position in thedisplay positions, where a maximum quantity of forward operations areperformed on an application program displayed at the display position.

According to a third aspect, an application program sorting apparatus,and the application program sorting apparatus includes a processor, amemory, a bus, and a communications interface. The processor, thememory, and the communications interface are connected by using the bus.The memory is configured to store computer program code, the computerprogram code includes an instruction, and when the processor of theapplication program sorting apparatus executes the instruction, theapplication program sorting apparatus performs the application programsorting method in the first aspect and the implementations of the firstaspect.

According to a fourth aspect, a computer storage medium is provided. Thecomputer storage medium stores computer program code, the computerprogram code includes an instruction, and when the processor of theapplication program sorting apparatus in the third aspect executes theinstruction, the application program sorting apparatus performsapplication program sorting method in the first aspect and the possibleimplementations of the first aspect.

It should be noted that the processor in the third aspect of thisapplication may be integration of function modules such as the obtainingmodule, the calculation module, the correction module, and the sortingmodule in the second aspect, and the processor may implement functionsof the function modules in the second aspect. For detailed descriptionsof the modules in the second aspect and the third aspect and analysis ofbeneficial effects, refer to corresponding descriptions and technicaleffects in the first aspect and the possible implementations of thefirst aspect. Details are not described herein again.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of a network architecture of anapplication program recommendation system according to an embodiment ofthe present invention;

FIG. 2 is a schematic diagram of an application recommendation interfaceof a terminal device according to an embodiment of the presentinvention;

FIG. 3 is a flowchart 1 of an application program sorting methodaccording to an embodiment of the present invention;

FIG. 4 is a flowchart 2 of an application program sorting methodaccording to an embodiment of the present invention;

FIG. 5 is a flowchart 3 of an application program sorting methodaccording to an embodiment of the present invention;

FIG. 6 is a flowchart 4 of an application program sorting methodaccording to an embodiment of the present invention;

FIG. 7 is a flowchart 5 of an application program sorting methodaccording to an embodiment of the present invention;

FIG. 8 is a flowchart 6 of an application program sorting methodaccording to an embodiment of the present invention;

FIG. 9 is a schematic structural diagram 1 of an application programsorting apparatus according to an embodiment of the present invention;

FIG. 10 is a schematic structural diagram 2 of an application programsorting apparatus according to an embodiment of the present invention;and

FIG. 11 is a schematic structural diagram 3 of an application programsorting apparatus according to an embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

An application program sorting method and apparatus are provided in thefollowing embodiments, which can be applied to an application programrecommendation process, and is specifically applied to an applicationprogram sorting process before an application program is recommended.

Terms in the embodiments of the present invention are described below.

An “application program recommendation interface” may be a displayinterface of a terminal device, that is used to display an applicationprogram and that displays software such as an application marketinstalled on the terminal device.

A “forward operation” may be specifically an operation that can reflecta will of a user to use an application program. For example, the“forward operation” may be an operation such as tapping, downloading,and payment. A “non-forward operation” may be an operation such asbrowsing.

A “data sample” may be a set of information, on a terminal device,related to an operation performed by a user on an application program.

For example, each of at least two data samples corresponds to one userand one application program. The data sample includes feature indicationinformation of a plurality of features, and the plurality of featuresmay be at least three features. For example, the at least three featuresinclude at least one feature of the user, least one feature of theapplication program, and a feature of at least one scenario.

The at least one feature of the user may include a gender, an age, orthe like of the user. The at least one feature of the applicationprogram may be a type of the application program, an icon (such as anicon color or an icon shape) of the application program, or the like.The feature of the at least one scenario may be environmentalparameters, such as time, a position, and weather in which the useroperates the application program, that may affect “whether the userperforms a positive operation on the application program”.

For example, in the disclosed embodiments, a data sample correspondingto a user i and an application program j may be represented by usingX_((i,j)), i∈{1, 2, . . . , m}, m is used to represent a quantity ofusers, m≥2, j∈{1, 2, . . . , n}, n is used to represent a quantity ofapplication programs, and n≥2. Feature indication information of ak^(th) same feature x_((i,j)-k) in X_((i,j)) may be represented by usinga_((i,j)-k), k∈{1, 2, . . . , q}, q is used to represent a quantity offeatures in X_((i,j)), and q≥3.

Specifically, the data sample X_((i,j))={x_(1(i,j)), x_(2(i,j)),x_(3(i,j))}, where x_(1(i,j)) is used to represent a feature of the useri, x_(2(i,j)) is used to represent a feature of the application programj, and x_(3(i,j)) is used to represent a scenario in which the user ioperates the application program j. For example, it is assumed that theat least one feature of the user included in the data sample may includea hair color, a gender, and an age group of the user; the at least onefeature of the application program may include an icon color and a typeof the application program; the feature of the at least one scenario mayinclude time, weather, or the like in which the user performs a positiveoperation or a non-positive operation on the application program.

For example, the hair color may include black, yellow, and white; thegender includes male and female; and the age group may be the juvenile,the young, or the aged. The icon color of the application program may beblue or red, and the type of the application program may be a socialtype, a game type, or an office type. The time when the user performs apositive operation or a non-positive operation on the applicationprogram may be in the morning, in the afternoon, or in the evening.

It is assumed that the feature x_(1(i,j)) of the user i in the datasample X_((i,j)) may include eight features: x_((i,j)-1), x_((i,j)-2),x_((i,j)-3), x_((i,j)-4), x_((i,j)-5), x_((i,j)-6), x_((i,j)-7), andx_((i,j)-8); the feature x_(2(i,j)) of the application program j mayinclude five features: x_((i,j)-9), x_((i,j)-10), x_((i,j)-11),x_((i,j)-12), and x_((i,j)-13); and the scenario x_(3(i,j)) in which theuser i operates the application program j may include three features:x_((i,j)-14), x_((i,j)-15), and x_((i,j)-16).

x_((i,j)-1) represents “whether the user i has black hair”, x_((i,j)-2)represents “whether the user i has yellow hair”, x_((i,j)-3) represents“whether the user i has white hair”, x_((i,j)-4) represents “whether theuser i is male”, x_((i,j)-5) represents “whether the user i is female”,x_((i,j)-6) represents “whether the user i is juvenile”, x_((i,j)-7)represents “whether the user i is young”, and x_((i,j)-8) represents“whether the user i is aged”.

x_((i,j)-9) represents “whether the icon of the application program j isblue”, x_((i,j)-10) represents “whether the icon of the applicationprogram j is red”, x_((i,j)-11) represents “whether the applicationprogram j is a social-type application program”, x_((i,j)-12) represents“whether the application program j is a game-type application program”,and x_((i,j)-13) represents “whether the application program j is anoffice-type application program”.

x_((i,j)-14) represents “whether the time when the user i operates theapplication program j is in the morning”, x_((i,j)-15) represents“whether the time when the i operates the application program j is inthe afternoon”, and x_((i,j)-16) represents “whether the time when theuser i operates the application program j is in the evening”.

The features x_((i,j)-1), x_((i,j)-2), x_((i,j)-3), . . . ,x_((i,j)-15), and x_((i,j)-16) respectively correspond to featureindication information a_((i,j)-1), a_((i,j)-2), . . . , a_((i,j)-15),and. The feature indication information a_((i,j)-1), a_((i,j)-2), . . ., a_((i,j)-16), and x_((i,j)-16) being 1 indicates “yes”, and thefeature indication information a_((i,j)-1), a_((i,j)-2), . . . ,a_((i,j)-15), and x_((i,j)-16) being 0 indicates “no”. For example, whenthe data sample X_((i,j)) represents that a male young man with blackhair views a social-type application program whose icon color is blue inthe morning, the data sample X_((i,j)) may be represented by using (1,0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0). When the data sampleX_((i,j)) represents that a male juvenile with yellow hair views agame-type application program whose icon color is red in the evening,the data sample X_((i,j)) may be represented by using (0, 1, 0, 1, 0, 1,0, 0, 0, 1, 0, 1, 0, 0, 0, 1).

The plurality of features in the data sample are respectively the sameas a plurality of features in another data sample. Another data samplemay be any one of the at least two data samples except the data sample.A difference is that feature indication information of a same feature indifferent data samples may be different.

For example, a data sample X_((1,1)) and a data sample X_((2,3)) in theat least two data samples are used as an example. Both the data sampleX_((1,1)) and the data sample X_((2,3)) may include the followingfeatures: “whether the user is male”, “whether the user has black hair”,“whether the icon of the application program is red”, “whether the timewhen the user operates the application program is in the morning”, andso on.

However, the feature “whether the user is male” is used as an example,feature indication information (for example, a user 1 is male) of thefeature “whether the user is male” in the data sample X_((1,1)) may bedifferent from feature indication information (for example, a user 2 isnot male) of the feature “whether the user is male” in the data sampleX_((2,3)).

A “positive operation probability of a data sample” may be used torepresent a possibility that a user corresponding to the data sampleperforms a positive operation on an application program corresponding tothe data sample. For example, the positive operation probabilityP_((i,j)) of the data sample X_((i,j)) is used to represent apossibility that the user i performs a positive operation on theapplication program j.

“Positive operation feedback information of a data sample” may be usedto indicate whether a user corresponding to the data sample hasperformed, in a preset time period, a positive operation on anapplication program corresponding to the data sample. For example,positive operation feedback information y_((i,j)) of the data sampleX_((i,j)) is used to indicate whether the user i has performed apositive operation on the application program j in the preset timeperiod. When y_((i,j)) is 1, it may indicate that the user i hasperformed a positive operation on the application program j in thepreset time period. When y_((i,j)) is 0, it may indicate that the user ihas not performed a positive operation on the application program j inthe preset time period.

A method for determining, by a terminal device, whether the user hasperformed a positive operation on the application program in the presettime period may include the following: If the user has performed apositive operation on the application program once in the preset timeperiod, the terminal device may assign 1 to the positive operationfeedback information, indicating that “the user has performed a positiveoperation on the application program”; or if the user has not performeda positive operation on the application program in the preset timeperiod, the terminal device may assign 0 to the positive operationfeedback information, indicating that “the user has not performed apositive operation on the application program”.

Alternatively, a method for determining, by a terminal device, whetherthe user has performed a positive operation on the application programin the preset time period may include the following: If a quantity offorward operations performed by the user on the application program inthe preset time period reaches a value, the terminal device may assign 1to the positive operation feedback information, indicating that “theuser has performed a positive operation on the application program”; orif a quantity of forward operations performed by the user on theapplication program in the preset time period does not reach a value,the terminal device may assign 0 to the positive operation feedbackinformation, indicating that “the user has not performed a positiveoperation on the application program”.

That an application recommendation interface displays an applicationprogram in the embodiments of the present invention may be specificallyas follows: The application recommendation interface displays a name andan icon of the application program and an interface generated duringrunning of the application program.

FIG. 1 is a schematic diagram of a network architecture of anapplication program recommendation system according to an embodiment. Asshown in FIG. 1, an application program recommendation system 10includes an application recommendation server 11, at least twoapplication program servers 12, and at least two terminal devices 13.

In this embodiment, an application program sorting method is describedherein by using an example in which the terminal devices 13, theapplication program servers 12, and the application recommendationserver 11 interact with each other to recommend an application program.Specifically, the application recommendation server 11 obtains ahistorical operation record of a user that is on each of the at leasttwo terminal devices and that corresponds to an application program. Theapplication recommendation server 11 recommends a sorted applicationprogram to each of the at least two terminal devices 13, and displaysthe application program in each of application recommendation interfaceson the at least two terminal devices 13. When a positive operation isperformed on the application program displayed in the applicationrecommendation interface of the terminal device 13, the correspondingapplication program server 12 provides an application service for theapplication program.

For example, the terminal device 13 may be a terminal device such as aPersonal Computer (PC), a mobile phone, a tablet computer, and aportable computer.

The application program sorting method provided in the disclosedembodiments may be executed by an application program sorting apparatus.The application program sorting apparatus may be an applicationrecommendation server (such as the application recommendation server 11shown in FIG. 1). In addition, the application program sorting apparatusmay be a terminal device (such as the at least two terminal devices 13shown in FIG. 1) on which software that can provide a service such asapplication program download is installed, or a Central Processing Unit(CPU) of the terminal device, or a control module that is in theterminal device and that is configured to sort application programs. Inthis embodiment of the present invention, the application programsorting method provided in the embodiments of the present invention isdescribed by using an example in which the application recommendationserver performs the application program sorting method.

It should be noted that the application recommendation interface of theterminal device may include a plurality of areas, and different areashave different functions. For example, the application recommendationinterface may include an application program display area, and theapplication recommendation interface displays a plurality of sortedapplication programs to the user in the application program displayarea. The application program display area may include a plurality ofdisplay positions, and each display position may display one applicationprogram. Usually, different application programs are displayed atdifferent display positions. Each of the plurality of applicationprograms may have an identifier used to distinguish the applicationprogram from another application program, and different applicationprograms correspond to different identifiers. For example, theidentifier of the application program may be a name (such as an ID 1) oran icon of the application program.

For example, FIG. 2 is a schematic diagram of an applicationrecommendation interface of a terminal device according to an embodimentof the present invention. In this embodiment of the present invention,descriptions are provided herein by using only an example of anapplication recommendation interface provided by a mobile phone that isan example of the terminal device, and by displaying at least one sortedapplication program in the application recommendation interface.

As shown in FIG. 2, the application recommendation interface includes anarea 1, an area 2, an area 3, and an area 4. The area 1 is a menu area,and may provide a plurality of function selections for a user. Forexample, if the terminal device detects that “manage” in the area 1 istriggered, the terminal device may display, to the user, informationsuch as an application program that has been downloaded by the user andan application program that is being downloaded by the user. The area 2is a search area, and may provide an input box for the user to searchfor an application program. If the terminal device detects an identifierthat is of an application program (for example, the identifier of theapplication program is an ID 1) and that is entered into the input boxin the area 2, the terminal device may display the application programwhose identifier is the ID 1 to the user. The area 3 is an activityarea, and may display publicity information and the like of someapplication programs to the user. The area 4 is an application programdisplay area, and may display a plurality of sorted application programsto the user by using a plurality of display positions in the applicationrecommendation interface.

For example, the terminal device may display, to the user by using theapplication recommendation interface of the terminal device, 10application programs shown in FIG. 2. Specifically, in the applicationrecommendation interface in FIG. 2, an application program 4 whoseidentifier is an ID 4 is displayed at a display position 1, anapplication program 2 whose identifier is an ID 2 is displayed at adisplay position 2, an application program 10 whose identifier is an ID10 is displayed at a display position 3, an application program 1 whoseidentifier is an ID 1 is displayed at a display position 4, anapplication program 5 whose identifier is an ID 5 is displayed at adisplay position 5, an application program 8 whose identifier is an ID 8is displayed at a display position 6, an application program 7 whoseidentifier is an ID 7 is displayed at a display position 7, anapplication program 6 whose identifier is an ID 6 is displayed at adisplay position 8, an application program 9 whose identifier is an ID 9is displayed at a display position 9, and an application program 3 whoseidentifier is an ID 3 is displayed at a display position 10.

In the application program sorting method provided in the disclosedembodiments, a positive operation probability of each data sample may becorrected, so that a corrected positive operation probability can betterreflect a possibility that a user performs a positive operation on anapplication program. In addition, the application programs are sortedbased on corrected positive operation probabilities, so that anapplication program sorting result meeting a user preference may beobtained, that is, a possibility that the user performs a positiveoperation on an application program displayed in an applicationrecommendation interface of a terminal device of the user is larger.

An application program sorting method and apparatus provided in thedisclosed embodiments are described below in detail with reference tothe accompanying drawings by using specific embodiments and applicationscenarios of the embodiments.

An embodiment of an application program sorting method is disclosed, asshown in FIG. 3, in which the application program sorting methodincludes S301 to S304.

S301: An application recommendation server obtains a positive operationprobability and positive operation feedback information of each of atleast two data samples.

For example, as shown in FIG. 4, a method in which the applicationrecommendation server obtains the positive operation probability and thepositive operation feedback information of each of the at least two datasamples may include S301 a to S301 c, in other words, S301 in FIG. 3 mayinclude S301 a to S301 c.

S301 a: The application recommendation server obtains historicaloperation information of at least two application programs of each of atleast two terminal devices, to obtain the at least two data samples andthe positive operation feedback information of each of the at least twodata samples.

Each terminal device may store a historical operation record of anoperation performed by a corresponding user on an application program inan application recommendation interface of the terminal device. Theapplication recommendation server may analyze the historical operationrecord of each of the at least two terminal devices, to obtain thehistorical operation information of the at least two applicationprograms of each of the at least two terminal devices, so as to obtainthe at least two data samples and the positive operation feedbackinformation of each of the at least two data samples. The historicaloperation records of the at least two terminal devices may include aplurality of data samples. Usually, terminal devices are in a one-to-onecorrespondence with users, and different terminal devices correspond todifferent users.

It should be noted that the at least two data samples mentioned in thisembodiment of the present invention may be all data samples in thehistorical operation records of the at least two terminal devices; orthe at least two data samples may be some data samples in the historicaloperation records of the at least two terminal devices. This is notlimited in this embodiment of the present invention.

For example, the at least two terminal devices include a terminal device1 and a terminal device 2. The terminal device 1 may correspond to auser 1. The user 1 and an application program 1 whose identifier is anID 1, the user 1 and an application program 2 whose identifier is an ID2, and the user 1 and an application program 3 whose identifier is an ID3 may respectively form three data samples: a data sample X_((1,1)), adata sample X_((1,2)), and a data sample X_((1,3)) The terminal device 2may correspond to a user 2. The user 2 and the application program 1whose identifier is the ID 1, the user 2 and the application program 2whose identifier is the ID 2, and the user 2 and the application program3 whose identifier is the ID 3 may respectively form three data samples:a data sample X_((2,1)), a data sample X_((2,2)), and a data sampleX_((2,3)). Positive operation feedback information of the data sampleX_((1,1)) is y_((1,1)), positive operation feedback information of thedata sample X_((1,2)) is y_((1,2)), and positive operation feedbackinformation of the data sample X_((1,3)) is y_((1,3)). Positiveoperation feedback information of the data sample X_((2,1)) isy_((2,1)), positive operation feedback information of the data sampleX_((2,2)) is y_((2,2)), and positive operation feedback information ofthe data sample X_((2,3)) is y_((2,3)).

Based on the foregoing embodiment, the at least two data samples in thisembodiment of the present invention may include all data samples (inother words, the data samples X_((1,1)), X_((1,2)), X_((1,3)),X_((2,1)), X_((2,2)), and X_((2,3))) in the foregoing embodiment.Alternatively, the at least two data samples may include only some datasamples in the foregoing embodiment. For example, the at least two datasamples may include the data samples X_((1,1)), X_((1,2)), X_((2,2)),and X_((2,3)).

S301 b: The application recommendation server collects statistics aboutthe at least two data samples and the positive operation feedbackinformation of each of the at least two data samples, to obtain apositive operation proportion value of at least one same feature in aplurality of features in the at least two data samples.

For example, in a data sample X_((i,j))={x_(1(i,j)), x_(2(i,j)),x_(3(i,j))} and that is of a user i and an application program j, afeature x_(1(i,j)) of the user i may include at least one feature (suchas a feature x_((i,j)-a)), a feature x_(2(i,j)) of the applicationprogram j may include at least one feature (such as a featurex_((i,j)-b)), and a scenario x_(3(i,j)) in which the user i operates theapplication program j may include at least one feature (such as afeature x_((i,j)-c)). In other words, the data sample X_((i,j)) mayinclude at least three features (such as x_((i,j)-a), x_((i,j)-b), andx_((i,j)-c)). Each of the at least three features may affect “whetherthe user i performs a positive operation on the application program j”.

It is assumed that the data sample X_((i,j)) may include the threefeatures x_((i,j)-a), x_((i,j)-b), and x_((i,j)-c). In this case, when iis any value in {1, 2, . . . , m} and j is any value in {1, 2, . . . ,n}, the obtained x_((i,j)-a) is a same feature, the obtained x_((i,j)-b)is a same feature, and the obtained x_((i,j)-c) is a same feature.

For example, x_((i,j)-a) (i is sequentially set to each value in {1, 2,. . . , m}, and i is sequentially set to each value in {1, 2, . . . ,n}) may be a same feature; x_((i,j)-b) (i is sequentially set to eachvalue in {1, 2, . . . , m}, and j is sequentially set to each value in{1, 2, . . . , n}) may be a same feature; x_((i,j)-c) (i is sequentiallyset to each value in {1, 2, . . . , m}, and i is sequentially set toeach value in {1, 2, . . . , n}) may be a same feature.

The at least one same feature may include some or all of the pluralityof features.

In this embodiment of the present invention, when feature indicationinformation of any one of the plurality of features is 0, it indicatesthat the feature has no impact on “whether the user performs a positiveoperation on the application program”. Alternatively, when featureindication information of any one of the plurality of features is 1, itindicates that the feature may affect “whether the user performs apositive operation on the application program”. In addition, whenpositive operation feedback information of the data sample X_((i,j)) is1 (that is, the data sample X_((i,j)) is a forward data sample), itindicates that a feature whose feature indication information is 1 andthat is in the data sample X_((i,j)) has affected “whether the user iperforms a positive operation on the application program j”.

In this way, the application recommendation server may collectstatistics about a quantity η of forward data samples in the at leasttwo data samples, and for a k^(th) same feature x_((i,j)-k) (i issequentially set to a value in {1, 2, . . . , m}, j is sequentially setto a value in {1, 2, . . . , n}, and the k^(th) same feature x_((i,j)-k)may be any one of x_((i,j)-a), x_((i,j)-b), or x_((i,j)-c)) in each ofthe forward data samples, collect statistics about a quantity β_(k) offorward data samples, in the forward data samples, in which featureindication information a_((i,j)-k) of the k^(th) same featurex_((i,j)-k) is 1; and calculate a ratio of the quantity β_(k) that isobtained through statistics collection and that is of forward datasamples in which the feature indication information a_((i,j)-k) of thek^(th) same feature x_((i,j)-k)_is 1 to the quantity η of forward datasamples, to obtain a positive operation proportion value W_(k) that isof the k^(th) same feature x_((i,j)-k) and that affects the positiveoperation feedback information y_((i,j)) of the data sample X_((i,j))being 1. In other words, w_(k)=β_(k)/η, where k∈{1, 2, . . . , q}.

The positive operation proportion value x_((i,j)-k) of the feature w_(k)may represent a degree of an impact of the k^(th) same featurex_((i,j)-k) on “whether the user i performs a positive operation on theapplication program j”. A larger positive operation proportion valuew_(k) of the k^(th) feature x_((i,j)-k) indicates a greater impact ofthe k^(th) feature x_((i,j)-k) on “whether the user performs a positiveoperation on the application program”.

For example, when the at least one same feature is all same features inthe plurality of features, the application recommendation server maycalculate a positive operation proportion value (such as w_(a), w_(b),and w_(c)) of any one of the plurality of features (such as x_((i,j)-a),x_((i,j)-b), and x_((i,j)-c)) included in the at least two data samples,to obtain a positive operation proportion value set W=(w_(a), w_(b),w_(c)). In this case, a quantity of dimensions of the positive operationproportion value set W is the same as a quantity of dimensions of thedata sample X_((i,j)). That is, the quantity of dimensions of thepositive operation proportion value set W is the same as a quantity offeatures included in the data sample X_((i,j)).

It is assumed that a feature X_((i,j)-1) represents “whether the user iis male”, a feature x_((i,j)-2) represents “whether an icon of theapplication program j is blue”, and a feature x_((i,j)-3) represents“whether a time at which the user i operates the application program jis in the morning”.

In addition, X_((1,1))=(1,0,1), and the positive operation feedbackinformation y_((1,1)) of X_((1,1)) is 0; X_((1,2))=(1,0,1), and thepositive operation feedback information y_((1,2)) of X_((1,2)) is 1;X_((1,3))=(0,1,0) and the positive operation feedback informationy_((1,3)) of X_((1,3)) is 1; X_((2,1))=(0,1,0), and the positiveoperation feedback information y_((2,1)) of X_((2,1)) is 1;X_((2,2))=(1,1,1), and the positive operation feedback informationy_((2,2)) of X_((2,2)) is 0; and X_((2,3))=(1,1,0), and the positiveoperation feedback information y_((2,3)) of X_((2,3)) is 1.

Table 1 shows a data sample list corresponding to the at least two datasamples provided herein in this embodiment of the present invention.

TABLE 1 Data sample list Application program j Application ApplicationApplication program 1 program 2 program 3 User i (j = 1) (j = 2) (j = 3)User 1 (i = 1) Data sample Data sample Data sample X_((1,1)) = (1, 0, 1)X_((1,2)) = (1, 0, 1) X_((1,3)) = (0, 1, 0) User 2 (i = 2) Data sampleData sample Data sample X_((2,1)) = (0, 1, 0) X_((2,2)) = (1, 1, 1)X_((2,3)) = (1, 1, 0)

Table 2 shows a data sample information list corresponding to the atleast two data samples provided herein in this embodiment of the presentinvention. The data sample information list may include the featureindication information of all the features in the at least two datasamples and the positive operation feedback information of the at leasttwo data samples.

TABLE 2 Data sample information list Feature Feature Feature Positiveindication indication indication operation information informationinformation feedback a_((i,j)−1) of an a_((i,j)−2) of a a_((i,j)−3) of ainfor- Data first same second same third same mation sample X_((i,j))feature x_((i,j)−1) feature x_((i,j)−2) feature x_((i,j)−3) y_((i,j))X_((1,1)) = (1, 0, 1) a_((1,1)−1) = 1 a_((1,1)−2) = 0 a_((1,1)−3) = 1y_((1,1)) = 0 X_((1,2)) = (1, 0, 1) a_((1,2)−1) = 1 a_((1,2)−2) = 0a_((1,2)−3) = 1 y_((1,2)) = 1 X_((1,3)) = (0, 1, 0) a_((1,3)−1) = 0a_((1,3)−2) = 1 a_((1,3)−3) = 0 y_((1,3)) = 1 X_((2,1)) = (0, 1, 0)a_((2,1)−1) = 0 a_((2,1)−2) = 1 a_((2,1)−3) = 0 y_((2,1)) = 1 X_((2,2))= (1, 1, 1) a_((2,2)−1) = 1 a_((2,2)−2) = 1 a_((2,2)−3) = 1 y_((2,2)) =0 X_((2,3)) = (1, 1, 0) a_((2,3)−1) = 1 a_((2,3)−2) = 1 a_((2,3)−3) = 0y_((2,3)) = 1

The application recommendation server may collect statistics about aquantity of data samples (in other words, X_((1,2)), X_((1,3)),X_((2,1)), and X_((2,3))) whose positive operation feedback informationis 1 and that are in the six data samples in Table 2, in other words,η=4; collect statistics about a quantity (in other words, β₁=2) of datasamples in which the feature indication information a_((i,j)-1) of theinitial same feature x_((i,j)-1) is 1 and that are in the four datasamples whose positive operation feedback information is 1; collectstatistics about a quantity (in other words, β₂=3) of data samples inwhich the feature indication information a_((i,j)-2) of the second samefeature x_((i,j)-2) is 1 and that are in the four data samples whosepositive operation feedback information is 1; collect statistics about aquantity (in other words, P₃=1) of data samples in which the featureindication information a_((i,j)-3) of the third same feature x_((i,j)-3)is 1 and that are in the four data samples whose positive operationfeedback information is 1; and calculate a positive operation proportionvalue w₁=β₁/η=2/4=0.5 of the initial same feature x_((i,j)-1), apositive operation proportion value w₂=β₂/η=3/4=0.75 of the second samefeature x_((i,j)-2), and a positive operation proportion valuew₃=β₃/η=1/4=0.25 of the third same feature x_((i,j)-3), to obtain apositive operation proportion value set W=(w₁, w₂, w₃)=(0.5, 0.75,0.25).

S301 c: The application recommendation server performs the followingoperation on each of the at least two data samples: calculating apositive operation probability of a first data sample based on thepositive operation proportion value of the at least one same feature inthe plurality of features in the at least two data samples.

The first data sample is any one of the at least two data samples. Thefirst data sample is the data sample X_((i,j)) of the user i and theapplication program j, i∈{1, 2, . . . , m}, m is used to represent aquantity of users, m≥2, j∈{1, 2, . . . , n}, n is used to represent aquantity of application programs, and n 2.

The application recommendation server may calculate a positive operationprobability P_((i,j)) of the data sample X_((i,j)) by using

$P_{({i,j})} = {{\sum\limits_{k = 1}^{q}\left( {w_{k} \times a_{{({i,j})} - k}} \right)} = {{w_{a} \times a_{{({i,j})} - a}} + {w_{b} \times a_{{({i,j})} - b}} + {w_{c} \times {a_{{({i,j})} - c}.}}}}$

When the positive operation proportion value set W may be a matrix ofone row and a plurality of columns, and the data sample X_((i,j)) is amatrix of one column and a plurality of rows, the applicationrecommendation server may further calculate the positive operationprobability P_((i,j)) of the data sample X_((i,j)) by usingP_((i,j))=W·X_((i,j)) because the quantity of dimensions of the positiveoperation proportion value set W is the same as the quantity ofdimensions of the data sample X_((i,j)), that is, the quantity ofdimensions of W is the same as the quantity of features included in thedata sample X_((i,j)).

For example, when a set of proportion values of forward operationsperformed by the user on the application program is a matrix W=(w₁ w₂w₃)=(0.5 0.75 0.25), and the data sample

${X_{({1,1})} = {\begin{pmatrix}a_{{({1,1})} - 1} \\a_{{({1,1})} - 2} \\a_{{({1,1})} - 3}\end{pmatrix} = \begin{pmatrix}1 \\0 \\1\end{pmatrix}}},$a positive operation probability of the data sample X_((1,1)) isP_((1,1))=w₁×a_((1,1)-1)+w₂×a_((1,1)-2)+w₃×a_((i,j)-3)=0.5×1+0.75×0+0.25×1=0.75.

Optionally, in the method in this embodiment of the present invention,the positive operation probability of the data sample may be furthernormalized, so that the positive operation probability of the datasample falls within a value range of 0 to 1. In this way, a normalizedpositive operation probability, of the data sample, falling within thevalue range of 0 to 1 helps simplify calculation related to the positiveoperation probability of the data sample in this embodiment of thepresent invention.

S302: The application recommendation server performs the followingoperation on each of the at least two data samples: calculating anuncertainty parameter of a positive operation probability of a firstdata sample based on the positive operation probabilities of the atleast two data samples, the positive operation feedback information ofthe at least two data samples, and feature indication information of atleast one same feature in the plurality of features in the at least twodata samples.

The first data sample is any one of the at least two data samples.

For example, as shown in FIG. 5, S302 in FIG. 3 may include S302 a andS302 b:

S302 a: The application recommendation server performs the followingoperation on each of the at least one same feature: calculating a weightof a first same feature based on the positive operation probabilities ofthe at least two data samples, the positive operation feedbackinformation of the at least two data samples, and feature indicationinformation of the first same feature in each of the at least two datasamples.

The first same feature is any one of the at least one same feature.

For example, the at least one same feature herein in this embodiment ofthe present invention may be all same features in the plurality offeatures. When the user has performed a positive operation on theapplication program, a larger quantity of times that any feature appearsin a plurality of data samples indicates a greater impact of the featureon “whether the user performs a positive operation on the applicationprogram”. In addition, a smaller difference between the positiveoperation probability and the positive operation feedback information ofany data sample indicates higher accuracy of the positive operationprobability of the data sample. Therefore, the applicationrecommendation server may calculate a weight n_(k) of a k^(th) samefeature in the at least two data samples by using

$n_{k} = {\sum\limits_{j = 1}^{n}{\sum\limits_{i = 1}^{m}{\left( {\left( {P_{({i,j})} - y_{({i,j})}} \right) \times \; a_{{({i,j})} - k}} \right)^{2}.}}}$

X_((i,j)) is used to represent a data sample corresponding to a user iand an application program j, i∈{1, 2, . . . , m}, m is used torepresent a quantity of users, m≥2, j∈{1, 2, . . . , n}, n is used torepresent a quantity of application programs, and n≥2; P_((i,j)); isused to represent a positive operation probability of X_((i,j)) andy_((i,j)) is used to represent positive operation feedback informationof X_((i,j)); and a_((i,j)-k) is used to represent feature indicationinformation of the k^(th) same feature x_((i,j)-k) in X_((i,j)), k∈{1,2, . . . , q}, q is used to represent a quantity of features inX_((i,j)), and q≤3.

Specifically, when the feature indication information a_((i,j)-k) of thek^(th) same feature x_((i,j)-k) in the data sample X_((i,j)) is 1, itindicates that the k^(th) same feature x_((i,j)-k) affects the positiveoperation probability P_((i,j)) of the data sample X_((i,j)). Adifference P_((i,j))−y_((i,j)) between the positive operationprobability P_((i,j)) and the positive operation feedback informationy_((i,j)) of the data sample X_((i,j)) may reflect accuracy of thepositive operation probability P_((i,j)). For example, a smallerP_((i,j))−y_((i,j)) indicates higher accuracy of the positive operationprobability P_((i,j)). In this case, the weight of n_(k) of the k^(th)same feature x_((i,j)-k) may be obtained by using the feature indicationinformation a_((i,j)-k) of the k^(th) same feature x_((i,j)-k) in thedata sample X_((i,j)) and the difference P_((i,j))−y_((i,j)) between thepositive operation probability P_((i,j)) and the positive operationfeedback information y_((i,j)) of the data sample X_((i,j)).

S302 b: The application recommendation server performs the followingoperation on each of the at least two data samples: calculating theuncertainty parameter of the positive operation probability of the firstdata sample based on a weight of the at least one same feature andfeature indication information of the at least one same feature includedin the first data sample.

When the user has performed a positive operation on the applicationprogram (for example, the positive operation feedback informationy_((i,j)) of the data sample X_((i,j)) is 1), a higher weight of anyfeature (such as the k^(th) same feature X_((i,j)-k)) in the pluralityof data samples indicates a smaller impact of the feature (the k^(th)same feature x_((i,j)-k)) on an uncertainty of a probability that “theuser performs a positive operation on the application program”.Therefore, the application recommendation server may calculate theuncertainty parameter uc_((i,j)) of the positive operation probabilityP_((i,j)) of the data sample X_((i,j)) by using

${uc}_{({i,j})} = {\sum\limits_{k = 1}^{q}{\frac{a_{{({i,j})} - k}}{\sqrt{n_{k}}}.}}$n_(k) is used to represent the weight of the k^(th) same featurex_((i,j)-k) in X_((i,j)).

The application recommendation server may further normalize theuncertainty parameter uc_((i,j)) of the positive operation probabilityP_((i,j)) to a value from 0 to 1. A normalization method may be asfollows: When the uncertainty parameter uc_((i,j)), is greater than 1,the uncertainty parameter uc_((i,j)) is directly set to 1; or when theuncertainty parameter uc_((i,j)) is not greater than 1, uc_((i,j)) isdivided by a maximum value in {uc_((i,j))} by using

${uc}_{({i,j})} = {\frac{{uc}_{({i,j})}}{\max_{i}\left\{ {uc}_{({i,j})} \right\}}.}$

In this way, a normalized uncertainty parameter uc_((i,j)) that is ofthe positive operation probability P_((i,j)) and that falls within thevalue range of 0 to 1 helps simplify calculation related to theuncertainty parameter uc_((i,j)) of the positive operation probabilityP_((i,j)) in this embodiment of the present invention.

S303: The application recommendation server performs the followingoperation on each of the at least two data samples: correcting thepositive operation probability of the first data sample by using theuncertainty parameter of the positive operation probability of the firstdata sample, to obtain a corrected positive operation probability of thefirst data sample.

Specifically, the application recommendation server may correct thepositive operation probability P_((i,j)) of the data sample X_((i,j)) byusing P_((i,j)) ¹=P_((i,j))×uc_((i,j)), to obtain a corrected positiveoperation probability P_((i,j)) ¹ of X_((i,j)). The corrected positiveoperation probability P_((i,j)) ¹ can better reflect a possibility thatthe user performs a positive operation on the application program. Thefirst data sample is the data sample X_((i,j)) of the user i and theapplication program j, i∈{1, 2, . . . , m}, m is used to represent thequantity of users, m≥2, j∈{1, 2, . . . , n}, n is used to represent thequantity of application programs, and n≥2; and uc_((i,j)) is used torepresent the uncertainty parameter of the positive operationprobability P_((i,j)) of X_((i,j)).

S304: The application recommendation server sorts, based on correctedpositive operation probabilities of the at least two data samples, allapplication programs corresponding to the at least two data samples.

Specifically, the application recommendation server may sort, indescending order of the corrected positive operation probabilities ofall the data samples, the application programs corresponding to the atleast two data samples, and then display the sorted application programsto the user by using the application recommendation interface.

A larger corrected positive operation probability of the data sampleindicates a larger possibility that the user corresponding to the datasample performs a positive operation on the application programcorresponding to the data sample. Therefore, when the applicationprograms are sorted in descending order of the corrected positiveoperation probabilities of the data samples, an application program onwhich the user is more likely to perform a positive operation may rankhigher.

In the application program sorting method provided in this embodiment ofthe present invention, the uncertainty parameter of the positiveoperation probability of each data sample may be calculated based on thepositive operation probabilities of the at least two data samples, thepositive operation feedback information of the at least two datasamples, and the feature indication information of the at least one samefeature in the plurality of features in the at least two data samples,and the positive operation probability of the data sample is thencorrected by using the uncertainty parameter of each positive operationprobability, to obtain the corrected positive operation probability ofthe data sample. Compared with the uncorrected positive operationprobability, the corrected positive operation probability can moreaccurately reflect the possibility that the user performs a positiveoperation on the application program. Therefore, compared with anapplication program sorting result obtained after the applicationprograms are sorted by using the uncorrected positive operationprobabilities, an application program sorting result obtained after allthe application programs corresponding to the at least two data samplesare sorted based on the corrected positive operation probabilities ofthe at least two data samples can increase the possibility that the userperforms a positive operation on the application program.

Further, in addition to the uncertainty of the positive operationprobability, a user attention degree of a display position also affectsthe positive operation probability of the data sample. For example, apossibility that a positive operation is performed on an applicationprogram displayed at a higher display position in the applicationrecommendation interface is usually greater than a possibility that apositive operation is performed on an application program displayed at alower display position. Therefore, in this embodiment of the presentinvention, the positive operation probability of the data sample may befurther corrected by using the user attention degree of the displayposition. Specifically, before S303, the method in this embodiment ofthe present invention may further include S305, and S303 may becorrespondingly replaced with S303′.

For example, as shown in FIG. 6, before S303 shown in FIG. 3, the methodin this embodiment of the present invention may further include S305.Correspondingly, as shown in FIG. 6, S303 in FIG. 3 may be replaced withS303′.

S305: The application recommendation server calculates a user attentiondegree of each display position that is used to display an applicationprogram and that is in an application recommendation interface of theterminal device.

For example, the application recommendation server may calculate, in thefollowing manners, the user attention degree of each display positionthat is used to display an application program and that is in theapplication recommendation interface of the terminal device.

Manner 1: The application recommendation server obtains a quantity offorward operations performed in a time period on an application programdisplayed at each display position. The quantity of forward operationsperformed on the application program displayed at each display positionis used to represent the user attention degree of the display position.

Manner 2: The application recommendation server obtains a quantity offorward operations performed in a time period on an application programdisplayed at each display position, and calculates a ratio of thequantity of forward operations performed on the application programdisplayed at each display position to a quantity of forward operationsperformed on an application program displayed at a first displayposition. A ratio of a quantity of forward operations performed on anapplication program displayed at any display position to the quantity offorward operations performed on the application program displayed at thefirst display position is used to represent the user attention degree ofthe display position. The first display position is a display positionin the display positions, where a maximum quantity of forward operationsare performed on an application program displayed at the displayposition.

The time period may be before the application recommendation servercalculates the user attention degree of each display position, andduration of the time period may be preset.

It may be figured out that before the application recommendation servercalculates the user attention degree of each display position, theapplication recommendation server may obtain, once in a time period, thequantity of forward operations performed on the application programdisplayed at each display position; or the application recommendationserver may obtain, once at intervals, the quantity of forward operationsperformed on the application program displayed at each display positionin the application recommendation interface of the terminal device.

When the application recommendation server displays the applicationprogram in the application recommendation interface of the applicationrecommendation server, the application recommendation server usually maydisplay an application program with a relatively high user score at arelatively high display position, and display an application programwith a relatively low user score at a relatively low display position.In other words, in addition to the display position, factors affectingwhether the user performs a positive operation on the applicationprogram may further include a user score. To eliminate an impact of theuser score on that the user performs a positive operation on theapplication program, the application recommendation server may firstrandomly display each application program at each display position inthe application recommendation interface in a random sorting manner, andthen collect statistics about the quantity of forward operationsperformed by the user on the application program at each displayposition, to obtain the user attention degree of each display position.

For example, it is assumed that the application recommendation interfaceincludes N display positions. The application recommendation server mayevenly divide, into N portions, all users to which an applicationprogram may be recommended, and each portion of users may include aplurality of users. When displaying an application program in theapplication recommendation interface for the first time, the applicationrecommendation server may fixedly display the application program at aninitial first display position (such as the display position 1 in FIG.2) to one of the N portions of users. When displaying the sameapplication program in the application recommendation interface for thesecond time, the application recommendation server may fixedly displaythe application program at a second display position (such as thedisplay position 2 in FIG. 2) to another portion of the N portions ofusers, until the application program is displayed at each of the Ndisplay positions after the application program is displayed in theapplication recommendation interface N times. Different users maycorrespond to different user identities, and an identity of the user maybe a user name registered when the user uses software such as anapplication market installed on the terminal device. That “all the usersto which an application program may be recommended may be evenly dividedinto N portions” may be that the application recommendation server mayevenly divide, into N portions, user identities of all the users towhich an application program may be recommended.

The application recommendation server may collect statistics about aquantity of forward operations performed, on an application programdisplayed at each display position (which is referred to as a quantityof forward operations performed at the display position for short), in aspecific time (such as one day, one week, or one month) after theapplication program is displayed in the application recommendationinterface each time. The quantity of forward operations performed on theapplication program displayed at each display position may be used asthe user attention degree of the display position.

Further, the application recommendation server may normalize thequantity of forward operations performed at each display position, anddetermine, as the user attention degree of the display position, aresult obtained after the quantity of forward operations performed ateach display position is normalized.

For example, the application recommendation server may normalize, byusing

${{b\left\{ {{pos}\left( {j,l} \right)} \right\}} = \frac{{pos\_ count}\;\left\{ {{pos}\left( {j,l} \right)} \right\}}{\max_{T}\left\{ {{pos\_ count}\;\left( {{pos}\left( {j,l} \right)} \right)} \right\}}},$a quantity pos count{pos(j,l)} of forward operations performed on anapplication program at a display position pos(j,l), to obtain a userattention degree b{pos(j,l)} of the display position.

b{pos(j,l)} is used to represent the user attention degree of thedisplay position pos(j,l) at which the application program j isdisplayed for the l^(th) time in the application recommendationinterface, l∈{1, 2, . . . , T}, T is used to represent a quantity oftimes of displaying the application program j in the applicationrecommendation interface, and T≥1; pos count({pos(j,l)} is used torepresent the quantity of forward operations performed on theapplication program at the display position pos(j,l); max, {poscount(pos(j,l))} is used to represent a maximum quantity of forwardoperations performed on the application program j displayed T times at adisplay position in the application recommendation interface.

In this way, the user attention degree b{pos(j,l)} that is of thedisplay position and that is obtained by normalizing the quantity poscount{pos(j,l)} of forward operations performed on the applicationprogram at the display position pos(j,l) helps simply calculationrelated to the user attention degree of the display position in thisembodiment of the present invention.

For example, it is assumed that the application recommendation interfaceincludes N display positions and M to-be-displayed application programs,and N≤M. The application recommendation server may randomly select oneapplication program from the M to-be-displayed application programs,randomly select one display position from the N display positions, anddisplay the selected application program at the selected displayposition, until there is an application program displayed at each of theN display positions.

The application recommendation server may collect statistics about aquantity of forward operations performed at each display position in aspecific time (such as one day, one week, or one month). The quantity offorward operations performed at each display position may be used as theuser attention degree of the display position.

Further, the application recommendation server may normalize thequantity of forward operations performed at each display position, anddetermine, as the user attention degree of the display position, aresult obtained after the quantity of forward operations performed ateach display position is normalized. Herein in this embodiment of thepresent invention, for detailed descriptions of “normalizing thequantity of forward operations performed at each display position”,refer to the related descriptions in the foregoing embodiment. Detailsare not described herein.

S303′: The application recommendation server performs the followingoperation on each of the at least two data samples: correcting thepositive operation probability of the first data sample by using theuncertainty parameter of the positive operation probability of the firstdata sample and the user attention degree of each display position, toobtain a corrected positive operation probability of the first datasample.

Both the uncertainty of the positive operation probability of the datasample and the display position affect the positive operationprobability of the data sample. Therefore, the positive operationprobability of each data sample is corrected by using the uncertaintyparameter of the positive operation probability and the user attentiondegree of each display position, so as to weaken impacts of theuncertainty of the positive operation probability of each data sampleand each display position on the positive operation probability of thedata sample may be separately weakened, thereby improving accuracy ofthe positive operation probability of each data sample.

For example, the application recommendation server may correct apositive operation probability P_((i,j)) of a data sample X_((i,j)) byusing P_((i,j)) ^((l+1))=P_((i,j))×(uc_((i,j)))^(B) and

${B = {\sum\limits_{l = 1}^{T}{b\left\{ {{pos}\left( {j,l} \right)} \right\}}}},$to obtain a corrected positive operation probability P_((i,j)) ^((l+1))of X_((i,j)).

uc_((i,j)) is used to represent an uncertainty parameter of the positiveoperation probability P_((i,j)) of X_((i,j)); pos(j,l) is used torepresent a display position at which the application program j isdisplayed for the l^(th) time in the application recommendationinterface; and b{pos(j,l)} is used to represent a user attention degreeof the display position pos(j,l), l∈{1, 2, . . . , T}, T is used torepresent a quantity of times of displaying the application program j inthe application recommendation interface, and T≥1.

Further, in this embodiment of the present invention, the applicationrecommendation server may further sort, in a targeted manner based oncorrected positive operation probabilities of all data samplescorresponding to each user, application programs corresponding to allthe data samples of the user. Specifically, S304 may include S304 a andS304 b.

For example, as shown in FIG. 7, S304 in FIG. 3 may include S304 a andS304 b:

S304 a: The application recommendation server determines at least twosecond data samples from the at least two data samples.

Each of the at least two second data samples corresponds to a first userand one application program.

For example, the at least two data samples provided in the foregoingembodiment may include data samples X_((1,1)), X_((1,2)), X_((1,3)),X_((2,1)), X_((2,2)), and X_((2,3)). The application recommendationserver may determine, from the foregoing six data samples, the datasamples X_((1,1)), X_((1,2)), and X_((1,3)) that correspond to a user 1(i=1), and determine the data samples X_((2,1)), X_((2,2)), andX_((2,3)) that correspond to a user 2 (i=2). In this case, if the firstuser is the user 1, the at least two second data samples correspondingto the first user are the data samples X_((1,1)), X_((1,2)), andX_((1,3)).

S304 b: The application recommendation server sorts, based on correctedpositive operation probabilities of the at least two second datasamples, all application programs corresponding to the at least twosecond data samples.

A sorting result obtained after the application recommendation serversorts all the application programs corresponding to the at least twosecond data samples is used to recommend, to the first user, all theapplication programs corresponding to the at least two second datasamples.

For example, based on the foregoing embodiment, if the applicationrecommendation server sorts application programs corresponding to theuser 1 (i=1), the application recommendation server may sort anapplication program 1 whose identifier is an ID 1 and that correspondsto the data sample X_((1,1)), an application program 2 whose identifieris an ID 2 and that corresponds to the data sample X_((1,2)), and anapplication program 3 whose identifier is an ID 3 and that correspondsto the data sample X_((1,3)), and display (recommend), to the user 1 byusing the application recommendation interface, a sorting resultobtained after the application programs are sorted. Similarly, if theapplication recommendation server sorts application programscorresponding to the user 2 (i=2), the application recommendation servermay sort an application program 1 whose identifier is an ID 1 and thatcorresponds to the data sample X_((2,1)), an application program 2 whoseidentifier is an ID 2 and that corresponds to the data sample X_((2,2)),and an application program 3 whose identifier is an ID 3 and thatcorresponds to the data sample X_((2,3)), and display (recommend), tothe user 2 by using the application recommendation interface, a sortingresult obtained after the application programs are sorted.

In the application program sorting method provided in this embodiment,the uncertainty parameter of the positive operation probability of eachdata sample may be calculated based on the positive operationprobabilities of the at least two data samples, the positive operationfeedback information of the at least two data samples, and the featureindication information of the at least one same feature in the pluralityof features in the at least two data samples, and the positive operationprobability of the data sample is then corrected by using theuncertainty parameter of each positive operation probability, to obtainthe corrected positive operation probability of the data sample.Compared with the uncorrected positive operation probability, thecorrected positive operation probability can more accurately reflect thepossibility that the user performs a positive operation on theapplication program. Therefore, compared with an application programsorting result obtained after the application programs are sorted byusing the uncorrected positive operation probabilities, an applicationprogram sorting result obtained after all the application programscorresponding to the at least two data samples are sorted based on thecorrected positive operation probabilities of the at least two datasamples can increase the possibility that the user performs a positiveoperation on the application program.

Further, in addition to the positive operation probability, factorsaffecting whether the user performs a positive operation on theapplication program may include the display position at which theapplication program is located in the application recommendationinterface of the terminal device. Therefore, compared with the positiveoperation probability corrected based on only the uncertainty parameterof the positive operation probability of each data sample, the correctedpositive operation probability of each data sample obtained bycorrecting the positive operation probability of the first data sampleby using the uncertainty parameter of the positive operation probabilityof each data sample and the user attention degree of each displayposition can more accurately reflect the possibility that the userperforms a positive operation on the application program. Therefore,herein in this embodiment of the present invention, compared with theapplication program sorting result obtained after the applicationprograms are sorted based on only the corrected positive operationprobability of the uncertainty parameter of the positive operationprobability, the application program sorting result obtained after allthe application programs corresponding to the at least two data samplesare sorted based on the corrected positive operation probabilities ofthe at least two data samples may further increase the possibility thatthe user performs a positive operation on the application program.

The foregoing mainly describes the solutions provided in the disclosedembodiments from the perspective of interaction between networkelements. It may be understood that to implement the foregoingfunctions, network elements such as the terminal device and theapplication recommendation server each include corresponding hardwarestructures and/or software modules for implementing the functions. Itshould be noted that the units and algorithm steps in the examplesdescribed with reference to the disclosed embodiments may be implementedby hardware or a combination of hardware and computer software. Whetherthe functions are performed by hardware or computer software drivinghardware depends on particular applications and design constraintconditions of the technical solutions. Different methods to implementthe described functions may be used for each particular application, butit should not be considered that the implementation goes beyond thescope of the embodiments.

In the disclosed embodiments, the application program sorting apparatusmay be divided into one or more modules according to the methodexamples. For example, each module may be obtained through division fora corresponding function, or two or more functions may be integratedinto one processing module. The integrated module may be implemented ina form of hardware, or may be implemented in a form of a softwaremodule. It should be noted that the module division in the embodimentsis an example, and is merely logical function division. There may beanother division manner in an actual implementation.

FIG. 9 is a possible schematic structural diagram of an applicationprogram sorting apparatus according to the foregoing embodiment. Theapplication program sorting apparatus may be an applicationrecommendation server, or may be a terminal device on which softwarethat can provide a service such as application program download isinstalled, or a CPU of the terminal device, or a control module that ison the terminal device and that is configured to sort applicationprograms.

As shown in FIG. 9, the application program sorting apparatus 900includes an obtaining module 901, a calculation module 902, a correctionmodule 903, and a sorting module 904. The obtaining module 901 isconfigured to support the application program sorting apparatus 900 inperforming S301, S301 a, S301 b, and S301 c in the foregoing embodiment,and/or in performing another process of the technology described in thisspecification. The calculation module 902 is configured to support theapplication program sorting apparatus 900 in performing S302, S302 a,S302 b, and S305 in the foregoing embodiment, and/or in performinganother process of the technology described in this specification. Thecorrection module 903 is configured to support the application programsorting apparatus 900 in performing S303 and S303′ in the foregoingembodiment, and/or in performing another process of the technologydescribed in this specification. The sorting module 904 is configured tosupport the application program sorting apparatus 900 in performingS304, S304 a, and S304 b in the foregoing embodiment, and/or inperforming another process of the technology described in thisspecification.

Further, as shown in FIG. 10, the calculation module 902 of theapplication program sorting apparatus 900 in FIG. 9 further includes aweight calculation submodule 9021 and an uncertainty parametercalculation submodule 9022. The weight calculation submodule 9021 isconfigured to support the application program sorting apparatus 900 inperforming S302 a in the foregoing embodiment, and/or in performinganother process of the technology described in this specification. Theuncertainty parameter calculation submodule 9022 is configured tosupport the application program sorting apparatus 900 in performing S302b in the foregoing embodiment, and/or in performing another process ofthe technology described in this specification.

Certainly, the application program sorting apparatus provided in thisembodiment of the present invention includes but is not limited to theforegoing function modules. For example, the application program sortingapparatus may further include a storage module. The storage module maybe configured to store information that includes a positive operationprobability of a data sample and a corrected positive operationprobability of the data sample and that is obtained when the applicationprogram sorting apparatus performs an application program sortingmethod. The application program sorting apparatus may further include acommunications interface. The communications interface may be configuredto support the application program sorting apparatus in sending anapplication program sorting result to a terminal device, or may beconfigured to support the application program sorting apparatus inreceiving information that is sent by a terminal device and thatincludes at least two data samples, positive operation feedbackinformation of each of the at least two data samples, and the like. Whenthe application program sorting apparatus is a terminal device on whichsoftware that can provide a service such as application program downloadis installed, the application program sorting apparatus may furtherinclude a display. The display may be configured to support theapplication program sorting apparatus in displaying a sorted applicationprogram.

When an integrated unit is used, the obtaining module 901, thecalculation module 902, the correction module 903, the sorting module904, and the like may be integrated into one processing module forimplementation. The processing module may be a processor or acontroller, for example, may be a CPU, a general-purpose processor, aDigital Signal Processor, (DSP), an Application-Specific IntegratedCircuit (EASIC), a Field Programmable Gate Array (FPGA) or anotherprogrammable logic device, a transistor logic device, a hardwarecomponent, or any combination thereof. The processing module mayimplement or execute various example logical blocks, modules, andcircuits that are described with reference to the content disclosed inthe present invention. The processing module may also be a combinationof computing functions, such as a combination of one or moremicroprocessors or a combination of a DSP and a microprocessor. Thestorage module may be a memory.

When the processing module is a processor and the storage module is amemory, an embodiment of the present invention provides an applicationprogram sorting apparatus 1100 shown in FIG. 11 (the application programsorting apparatus 1100 may be an application recommendation server). Asshown in FIG. 11, the application program sorting apparatus 1100includes a processor 1101, a memory 1102, a communications interface1103, and a bus 1104. The processor 1101, the memory 1102, and thecommunications interface 1103 are connected to each other by using thebus 1104. The bus 1104 may be a Peripheral Component Interconnect (PCI)bus, an Extended Industry Standard Architecture (EISA) bus, or the like.The bus 1104 may be categorized into an address bus, a data bus, acontrol bus, or the like. For ease of indication, the bus is indicatedby using only one bold line in FIG. 11. However, it does not indicatethat there is only one bus or only one type of bus.

An embodiment of the present invention further provides a computerstorage medium. The computer storage medium stores computer programcode. The computer program code includes an instruction. When theprocessor 1101 of the application program sorting apparatus 1100executes the instruction, the application program sorting apparatus 1100performs related method steps in the foregoing embodiment.

For detailed descriptions of the modules in the application programsorting apparatus 1100 provided in this embodiment of the presentinvention and technical effects brought after the modules performrelated method steps in the foregoing embodiment, refer to relateddescriptions in the method embodiment. Details are not described herein.

The foregoing description provide an understanding for implementing thedisclosed embodiments, and the division of the foregoing functionmodules is used as an example for illustration. In actual application,the foregoing functions can be allocated to different modules andimplemented according to a requirement, that is, an inner structure ofan apparatus is divided into different function modules to implement allor some of the functions described above. For a detailed working processof the foregoing system, apparatus, and module, refer to a correspondingprocess in the foregoing method embodiments. Details are not describedherein.

In the several embodiments, it should be understood that the disclosedsystem, apparatus, and method may be implemented in other manners. Forexample, the described apparatus embodiment is merely an example. Forexample, the module division is merely logical function division and maybe other division in actual implementation. For example, a plurality ofmodules or components may be combined or integrated into another system,or some features may be ignored or not performed. In addition, thedisplayed or discussed mutual couplings or direct couplings orcommunication connections may be implemented by using some interfaces.The indirect couplings or communication connections between theapparatuses or units may be implemented in electrical, mechanical, orother forms.

The function modules in the embodiments may be integrated into oneprocessing module, or each unit may exist alone physically, or two ormore modules are integrated into one unit. The integrated module may beimplemented in a form of hardware, or may be implemented in a form of asoftware function module.

When the integrated module is implemented in the form of a softwarefunction module and sold or used as an independent product, theintegrated module may be stored in a computer-readable storage medium.Based on such an understanding, the technical solutions of the presentinvention essentially, or the part contributing to the prior art, or allor some of the technical solutions may be implemented in the form of asoftware product. The computer software product is stored in a storagemedium, and includes several instructions for instructing a computerdevice (which may be a personal computer, a server, a network device, orthe like) or a CPU to perform all or some of the steps in theapplication program sorting method in the embodiments of the presentinvention. The foregoing storage medium includes: any medium that canstore program code, such as a USB flash drive, a removable hard disk, aRead-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk,or an optical disc.

The foregoing descriptions are merely specific embodiments, but are notintended to be limiting.

What is claimed is:
 1. An application sorting method, comprising:obtaining a positive operation probability and positive operationfeedback information of each of at least two data samples, wherein onedata sample in the at least two data samples corresponds to a user andan application program, the one data sample comprises feature indicationinformation of a plurality of features, the plurality of features in theone data sample are respectively the same as a plurality of features inanother data sample, the another data sample is any of the at least twodata samples except the one data sample, the positive operationprobability of the at least two data samples is a probability that theuser performs a positive operation on the application program in apreset time period, and the positive operation feedback information ofthe at least two data samples is used to indicate whether the user hasperformed a positive operation on the application in the preset timeperiod; calculating an uncertainty parameter of a positive operationprobability of a first data sample based on the positive operationprobabilities of the at least two data samples, the positive operationfeedback information of the at least two data samples, and featureindication information of at least one same feature in a plurality offeatures in the at least two data samples; correcting the positiveoperation probability of the first data sample by using the uncertaintyparameter of the positive operation probability of the first datasample, to obtain a corrected positive operation probability of thefirst data sample, wherein the first data sample is any of the at leasttwo data samples; and sorting, based on positive operation probabilitiesof the at least two data samples which comprise corrected positiveoperation probability of the first data sample, application programscorresponding to the at least two data samples.
 2. The method accordingto claim 1, further comprising: calculating a user attention degree ofdifferent display positions that are used to display an application andthat are in an application recommendation interface of a terminaldevice, wherein the user attention degree is used to represent apossibility that the user performs a positive operation on theapplication program displayed at the display position.
 3. The methodaccording to claim 2, further comprising: correcting the positiveoperation probability of the first data sample by using the uncertaintyparameter of the positive operation probability of the first data sampleand the user attention degrees of the different display positions, toobtain the corrected positive operation probability of the first datasample.
 4. The method according to claim 1, further comprising:calculating a weight of a first same feature based on the positiveoperation probabilities of the at least two data samples, the positiveoperation feedback information of the at least two data samples, andfeature indication information of the first same feature in each of theat least two data samples, wherein the first same feature is any of theat least one same feature; and calculating the uncertainty parameter ofthe positive operation probability of the first data sample based on aweight of the at least one same feature and the feature indicationinformation of the at least one same feature comprised in the first datasample.
 5. The method according to claim 4, wherein the at least onesame feature comprises each feature in the at least two data samples;and wherein the calculating the uncertainty parameter further comprises:calculating a weight n_(k) of a k^(th) same feature in the at least twodata samples by using${n_{k} = {\sum\limits_{j = 1}^{n}{\sum\limits_{i = 1}^{m}\left( {\left( {P_{({i,j})} - y_{({i,j})}} \right) \times \; a_{{({i,j})} - k}} \right)^{2}}}},$wherein k∈{1, 2, . . . , q}, q is used to represent a quantity offeatures in X_((i,j)), and q≥3, wherein X_((i,j)) is used to represent adata sample corresponding to a user i and an application j, i∈{1,2, . .. , m}, m is used to represent a quantity of users, m≥2, j∈{1,2, . . .n}, n is used to represent a quantity of application programs, and n≥2;P_((i,j)) is used to represent a positive operation probability ofX_((i,j)), and y_((i,j)) is used to represent positive operationfeedback information of X_((i,j)); and a_((i,j)-k) is used to representfeature indication information of the k^(th) same feature x_((i,j)-k) inX_((i,j)).
 6. The method according to claim 4, wherein the at least onesame feature comprises each feature in the at least two data samples;and wherein the calculating the uncertainty parameter further comprises:calculating an uncertainty parameter uc_((i,j)) of the positiveoperation probability of the data sample X_((i,j)) by using${{uc}_{({i,j})} = {\sum\limits_{k = 1}^{q}\frac{a_{{({i,j})} - k}}{\sqrt{n_{k}}}}},$wherein the first data sample is the data sample X_((i,j)) of the user iand the application j, i∈{1, 2, . . . , m}, m is used to represent aquantity of users, m≥2, j∈{1,2, . . . , n} is used to represent thequantity of application programs, and n≥2; k∈{1,2, . . . , n} q is usedto represent the quantity of features in X_((i,j)), and q≥3; n_(k) isused to represent the weight of the k^(th) same feature in X_((i,j));and a_((i,j)-k) is used to represent the feature indication informationof the k^(th) same feature x_((i,j)-k) in X_((i,j)).
 7. The methodaccording to claim 1, wherein the correcting the positive operationprobability of the first data sample further comprises: correcting thepositive operation probability P_((i,j)) of the data sample X_((i,j)) byusing P_((i,j)) ¹=P_((i,j))×uc_((i,j)), to obtain a corrected positiveoperation probability P_((i,j)) ¹ of X_((i,j)), wherein the first datasample is the data sample X_((i,j)) of the user i and the application j,i∈{1,2, . . . , m}, m is used to represent a quantity of users, m≥2,j∈{1, 2, . . . , n}, n is used to represent the quantity of applicationprograms, and n≥2; and uc_((i,j)) is used to represent the uncertaintyparameter of the positive operation probability P_((i,j)) of X_((i,j)).8. The method according to claim 2, wherein the correcting the positiveoperation probability of the first data sample further comprises:correcting a positive operation probability P_((i,j)) of a data sampleX_((i,j)) by using P_((i,j)) ^((l+1))=P_((i,j))×(uc_((i,j)))^(B)${B = {\sum\limits_{l = 1}^{T}{b\left\{ {{pos}\left( {j,l} \right)} \right\}}}},$to obtain a corrected positive operation probability P_((i,j)) ^(l+1))of X_((i,j)), wherein the first data sample is the data sample X_((i,j))of a user i and an application j, i∈{1,2, . . . , m}, m is used torepresent a quantity of users, m≥2, j∈{1,2, . . . , n}, n is used torepresent a quantity of application programs, and n≥2; uc_((i,j)) isused to represent an uncertainty parameter of the positive operationprobability P_((i,j)) of X_((i,j)); pos(j,l) is used to represent adisplay position at which the application j is displayed for the l^(th)time in the application recommendation interface; and b{pos(j,l)} isused to represent a user attention degree of the display positionpos(j,l), l∈{1, 2, . . . , T}, T is used to represent a quantity oftimes of displaying the application j in the application recommendationinterface, and T≥1.
 9. The method according to claim 7, wherein thesorting, based on corrected positive operation probabilities of the atleast two data samples, application programs corresponding to the atleast two data samples further comprises: determining at least twosecond data samples from the at least two data samples, wherein each ofthe at least two second data samples corresponds to a first user and anapplication program; and sorting, based on corrected positive operationprobabilities of the at least two second data samples, each applicationprogram corresponding to the at least two second data samples, wherein asorting result obtained after each application program corresponding tothe at least two second data samples are sorted is used to recommend, tothe first user, each application program corresponding to the at leasttwo second data samples.
 10. An application sorting apparatuscomprising: a processor; and a memory coupled to the processor andconfigured to store a plurality of instructions that, when executed,causes the processor to obtain a positive operation probability andpositive operation feedback information of each of at least two datasamples, wherein one data sample in the at least two data samplescorresponds to a user and an application program, the one data samplecomprises feature indication information of a plurality of features, theplurality of features in the one data sample are respectively the sameas a plurality of features in another data sample, the another datasample is any of the at least two data samples except the one datasample, the positive operation probability of the data sample is aprobability that the user performs a positive operation on theapplication program in a preset time period, and the positive operationfeedback information of the at least two data samples is used toindicate whether the user has performed a positive operation on theapplication program in the preset time period, calculate an uncertaintyparameter of a positive operation probability of a first data samplebased on the positive operation probabilities of the at least two datasamples, the positive operation feedback information of the at least twodata samples, and feature indication information of at least one samefeature in a plurality of features in the at least two data samples,correct the positive operation probability of the first data sample byusing the uncertainty parameter of the positive operation probability ofthe first data sample, to obtain a corrected positive operationprobability of the first data sample, wherein the first data sample isany one of the at least two data samples, and sort, based on positiveoperation probabilities of the at least two data samples which comprisecorrected positive operation probability of the first data sample,application programs corresponding to the at least two data samples. 11.The apparatus according to claim 10, wherein the processor is to:calculate a user attention degree of different display positions thatare used to display the application program and that are in anapplication recommendation interface of a terminal device, wherein theuser attention degree is used to represent a possibility that the userperforms a positive operation on the application program displayed atthe display position.
 12. The apparatus according to claim 11, whereinthe processor is to: correct the positive operation probability of thefirst data sample by using the uncertainty parameter of the positiveoperation probability of the first data sample and the user attentiondegrees of the different display positions, to obtain the correctedpositive operation probability of the first data sample.
 13. Theapparatus according to claim 10, wherein the processor is to: calculatecalculating a weight of a first same feature based on the positiveoperation probabilities of the at least two data samples, the positiveoperation feedback information of the at least two data samples, andfeature indication information of the first same feature in each of theat least two data samples, wherein the first same feature is any one ofthe at least one same feature; and calculate the uncertainty parameterof the positive operation probability of the first data sample based ona weight of the at least one same feature and the feature indicationinformation of the at least one same feature comprised in the first datasample.
 14. The apparatus according to claim 10, further comprising:correcting the positive operation probability P_((i,j)) of the datasample X_((i,j)) by using P_((i,j)) ¹=P_((i,j))×uc_((i,j)), to obtain acorrected positive operation probability P_((i,j)) ¹ of X_((i,j)),wherein the first data sample is the data sample X_((i,j)) of the user iand the application j, i∈{1,2, . . . , m}, m is used to represent aquantity of users, m≥2, j∈{1, 2, . . . , n}, n is used to represent thequantity of application programs, and n≥2; and uc_((i,j)) is used torepresent the uncertainty parameter of the positive operationprobability P_((i,j)) of X_((i,j)).
 15. A non-transitorycomputer-readable storage medium comprising instructions which, whenexecuted by a computer, cause the computer to perform an operationcomprising: obtaining a positive operation probability and positiveoperation feedback information of each of at least two data samples,wherein one data sample in the at least two data samples corresponds toa user and an application program, the one data sample comprises featureindication information of a plurality of features, the plurality offeatures in the one data sample are respectively the same as a pluralityof features in another data sample, the another data sample is any ofthe at least two data samples except the one data sample, the positiveoperation probability of the at least two data samples is a probabilitythat the user performs a positive operation on the application programin a preset time period, and the positive operation feedback informationof the at least two data samples is used to indicate whether the userhas performed a positive operation on the application program in thepreset time period; calculating an uncertainty parameter of a positiveoperation probability of a first data sample based on the positiveoperation probabilities of the at least two data samples, the positiveoperation feedback information of the at least two data samples, andfeature indication information of at least one same feature in aplurality of features in the at least two data samples; correcting thepositive operation probability of the first data sample by using theuncertainty parameter of the positive operation probability of the firstdata sample, to obtain a corrected positive operation probability of thefirst data sample, wherein the first data sample is any one of the atleast two data samples; and sorting, based on positive operationprobabilities of the at least two data samples which comprise correctedpositive operation probability of the first data sample, applicationprograms corresponding to the at least two data samples.
 16. Thenon-transitory computer-readable storage medium according to claim 15,wherein the computer is to perform an operation comprising: calculatinga user attention degree of different display positions that are used todisplay the application program and that are in an applicationrecommendation interface of a terminal device, wherein the userattention degree is used to represent a possibility that the userperforms a positive operation on the application program displayed atthe display position.
 17. The non-transitory computer-readable storagemedium according to claim 16, wherein the computer is to perform anoperation comprising: correcting the positive operation probability ofthe first data sample by using the uncertainty parameter of the positiveoperation probability of the first data sample and the user attentiondegrees of the different display positions, to obtain the correctedpositive operation probability of the first data sample.
 18. Thenon-transitory computer-readable storage medium according to claim 15,wherein the computer is to perform an operation comprising: calculatinga weight of a first same feature based on the positive operationprobabilities of the at least two data samples, the positive operationfeedback information of the at least two data samples, and featureindication information of the first same feature in each of the at leasttwo data samples, wherein the first same feature is any one of the atleast one same feature; and calculating the uncertainty parameter of thepositive operation probability of the first data sample based on aweight of the at least one same feature and the feature indicationinformation of the at least one same feature comprised in the first datasample.
 19. The non-transitory computer-readable storage mediumaccording to claim 15, wherein the computer is to perform an operationcomprising: correcting the positive operation probability P_((i,j)) ofthe data sample X_((i,j)) by using P_((i,j)) ¹=P_((i,j))×uc_((i,j)), toobtain a corrected positive operation probability P_((i,j)) ¹ ofX_((i,j)), wherein the first data sample is the data sample X_((i,j)) ofthe user i and the application j, i∈{1,2, . . . , m}, m is used torepresent a quantity of users, m≥2, j∈{1, 2, . . . , n}, n is used torepresent the quantity of application programs, and n≥2; and uc_((i,j))is used to represent the uncertainty parameter of the positive operationprobability P_((i,j)) of X_((i,j)).